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Collins, Brian Harris. "Thermal imagery spectral analysis." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1996. http://handle.dtic.mil/100.2/ADA320553.
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Thesis (M.S. in Systems Technology (Space Systems Operations)) Naval Postgraduate School, September 1996.Thesis advisor(s): R.C. Olsen, David Cleary. "September 1996." Includes bibliographical references (p. 159-161). Also available online.
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Behrens, Richard J. "Change detection analysis with spectral thermal imagery." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1998. http://handle.dtic.mil/100.2/ADA356044.
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Thesis (M.S. in Space Systems Operations) Naval Postgraduate School, September 1998."September 1998." Thesis advisor(s): Richard Christopher Olsen, David D. Cleary. Includes bibliographical references (p. 129-131). Also available online.
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Ward, Jason T. "Realistic texture in simulated thermal infrared imagery /." Online version of thesis, 2008. http://hdl.handle.net/1850/7067.
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Berg, Amanda. "Detection and Tracking in Thermal Infrared Imagery." Licentiate thesis, Linköpings universitet, Datorseende, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-126955.
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Thermal cameras have historically been of interest mainly for military applications. Increasing image quality and resolution combined with decreasing price and size during recent years have, however, opened up new application areas. They are now widely used for civilian applications, e.g., within industry, to search for missing persons, in automotive safety, as well as for medical applications. Thermal cameras are useful as soon as it is possible to measure a temperature difference. Compared to cameras operating in the visual spectrum, they are advantageous due to their ability to see in total darkness, robustness to illumination variations, and less intrusion on privacy. This thesis addresses the problem of detection and tracking in thermal infrared imagery. Visual detection and tracking of objects in video are research areas that have been and currently are subject to extensive research. Indications oftheir popularity are recent benchmarks such as the annual Visual Object Tracking (VOT) challenges, the Object Tracking Benchmarks, the series of workshops on Performance Evaluation of Tracking and Surveillance (PETS), and the workshops on Change Detection. Benchmark results indicate that detection and tracking are still challenging problems. A common belief is that detection and tracking in thermal infrared imagery is identical to detection and tracking in grayscale visual imagery. This thesis argues that the preceding allegation is not true. The characteristics of thermal infrared radiation and imagery pose certain challenges to image analysis algorithms. The thesis describes these characteristics and challenges as well as presents evaluation results confirming the hypothesis. Detection and tracking are often treated as two separate problems. However, some tracking methods, e.g. template-based tracking methods, base their tracking on repeated specific detections. They learn a model of the object that is adaptively updated. That is, detection and tracking are performed jointly. The thesis includes a template-based tracking method designed specifically for thermal infrared imagery, describes a thermal infrared dataset for evaluation of template-based tracking methods, and provides an overview of the first challenge on short-term,single-object tracking in thermal infrared video. Finally, two applications employing detection and tracking methods are presented.
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Durrenberger, Robert Earl 1951. "Absorption, Relaxation, and Imagery Instruction Effects on Thermal Imagery Experience and Finger Temperature." Thesis, North Texas State University, 1986. https://digital.library.unt.edu/ark:/67531/metadc332431/.
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A skill instruction technique based on cognitive behavioral principles was applied to thermal imagery to determine if it could enhance either subjective or physiological responsiveness. The effects of imagery instruction were compared with the effects of muscle relaxation on imagery vividness, thermal imagery involvement, and the finger temperature response. The subjects were 39 male and 29 female volunteers from a minimum security federal prison. The personality characteristic of absorption was used as a classification variable to control for individual differences. It was hypothesized that high absorption individuals would reveal higher levels of imagery vividness, involvement, and finger temperature change; that imagery skill instruction and muscle relaxation would be more effective than a control condition; and that the low absorption group would derive the greatest benefit from the imagery task instruction condition. None of the hypotheses was supported. Finger temperature increased over time during the experimental procedure but remained stable during thermal imagery. The results suggest that nonspecific relaxation effects may best account for finger temperature increases during thermal imagery. Results were discussed in relation to cognitive-behavioral theory and the characteristic of absorption.
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Christensson, Cornelis, and Albin Flodell. "Wildlife Surveillance Using a UAV and Thermal Imagery." Thesis, Linköpings universitet, Reglerteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-129586.
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På senare år har tjuvjakten på noshörningar resulterat i ett kritiskt lågt bestånd. Detta examensarbete är en del av ett initiativ för att stoppa denna utveckling. Målet är att använda en UAV, utrustad med GPS och attitydsensorer, samt en värmekamera placerad på en gimbal, till att övervaka vilda djur. Genom att använda en värmekamera kan djuren lätt detekteras eftersom de antas vara varmare än sin omgivning. En modell av marken vid testområdet har använts för att möjliggöra positionering av detekterade djur, samt analys av vilka områden på marken som ses av kameran. Termen övervakning inkluderar detektion av djur, målföljning och planering av rutt för UAV:n. UAV:n ska kunna söka av ett område efter djur. För att göra detta krävs planering av trajektoria för UAV:n samt hur gimbalen ska förflyttas. Flera metoder för detta har utvärderats. UAV:n ska även kunna målfölja djur som har detekterats. Till detta har ett partikelfilter använts. För att associera mätningar till spår har Nearest Neighbor-metoden använts. Djuren detekteras genom att bildbehandla på videoströmmen som ges från värmekameran. För bildbehandlingen har flertalet metoder testats. Dessutom presenteras en omfattande beskrivning av hur en UAV fungerar och är uppbyggd. I denna beskrivs även nödvändiga delar för ett UAV-system. På grund av begränsningar i budgeten har ingen UAV inköpts. Istället har tester utförts från en gondol i Kolmården. Gondolen åker runt i testområdet med en konstant hastighet. Djur kunde lätt detekteras och målföljas givet en kall bakgrund. Då solen värmer upp marken är det svårare att särskilja djuren från marken och fler feldetektioner görs av bildbehandlingenIn recent years, the poaching of rhinoceros has decreased its numbers to critical levels. This thesis project is a part of an initiative to stop this development. The aim of this master thesis project is to use a UAV equipped with positioning and attitude sensors as well as a thermal camera, placed onto a gimbal, to perform wildlife surveillance. By using a thermal camera, the animals are easily detected as they are assumed to be warmer than the background. The term wildlife surveillance includes detection of animals, tracking, and planning of the UAV. The UAV should be able to search an area for animals, for this planning of the UAV trajectory and gimbal attitude is needed. Several approaches for this have been tested, both online and offline planning. The UAV should also be able to track the animals that are detected, for this a particle filter has been used. Here a problem of associating measurements to tracks arises. This has been solved by using the Nearest Neighbor algorithm together with gating. The animals are detected by performing image processing on the images received from the thermal camera. Multiple approaches have been evaluated. Furthermore, a thoroughly worked description of how a UAV is working as well as how it is built up is presented. Here also necessary parts to make up a full unmanned aerial system are described. This chapter can be seen as a good guide for beginners, to the UAV field, interested in knowing how a UAV works and the most common parts of such a system. A ground model of Kolmården, where the testing has been conducted, has been used in this thesis. The use of this enables positioning of the detected animals and checking if an area is occluded for the camera. Unfortunately, due to budget limitations, no UAV was purchased. Instead, testing has been conducted from a gondola in Kolmården traveling across the test area with a constant speed. To use the gondola as the platform, for the sensors and the thermal camera, is essentially the same as using a UAV as both alternatives are located in the air above the animals, both are traveling around the map and both are stable for good weather conditions. The animals could easily be detected and tracked given a cold background. When the sun heats up the ground, it is harder to distinguish the animals in the thermal video, and more false detections in the image processing appear.
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Okyay, Unal. "Lithologic Discrimination And Mapping By Aster Thermal Infrared Imagery." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614549/index.pdf.
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In conventional remote sensing, visible-near infrared (VNIR) and shortwave infrared (SWIR) part of the electromagnetic spectrum (EMS) have been utilized for lithological discrimination extensively. Additionally, TIR part of the EM spectrum can also be utilized for discrimination of surface materials either through emissivity characteristics of materials or through radiance as in VNIR and SWIR. In this study, ASTER thermal multispectral infrared data is evaluated in regard to lithological discrimination and mapping through emissivity values rather than conventional methods that utilize radiance values. In order to reach this goal, Principle Component Analysis (PCA) and Decorrelation Stretch techniques are utilized for ASTER VNIR and SWIR data. Furthermore, the spectral indices which directly utilize the radiance values in VNIR, SWIR and TIR are also included in the image analysis. The emissivity values are obtained through Temperature-Emissivity Separation (TES) algorithm. The results of the image analyses, except spectral indices, are displayed in RGB color composite along with the geological map for visual interpretation. The results showed that utilizing emissivity values possesses potential for discrimination of organic matter bearing surface mixtures which has not been possible through the conventional methods. Additionally, PCA of emissivity values may increase the level of discrimination even further. Since the emissivity utilization is rather unused throughout in literature and new, further assessment of accuracy is highly recommended along with the field validations.
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Bergenroth, Hannah. "Use of Thermal Imagery for Robust Moving Object Detection." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177888.
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This work proposes a system that utilizes both infrared and visual imagery to create a more robust object detection and classification system. The system consists of two main parts: a moving object detector and a target classifier. The first stage detects moving objects in visible and infrared spectrum using background subtraction based on Gaussian Mixture Models. Low-level fusion is performed to combine the foreground regions in the respective domain. For the second stage, a Convolutional Neural Network (CNN), pre-trained on the ImageNet dataset is used to classify the detected targets into one of the pre-defined classes; human and vehicle. The performance of the proposed object detector is evaluated using multiple video streams recorded in different areas and under various weather conditions, which form a broad basis for testing the suggested method. The accuracy of the classifier is evaluated from experimentally generated images from the moving object detection stage supplemented with publicly available CIFAR-10 and CIFAR-100 datasets. The low-level fusion method shows to be more effective than using either domain separately in terms of detection results.Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet
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Magnabosco, Marina. "Self localization and mapping using optical and thermal imagery." Thesis, Cranfield University, 2011. http://dspace.lib.cranfield.ac.uk/handle/1826/6704.
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Given a mobile robot starting from an unknown position in an unknown environment, with
the task of explores the surroundings, it has to be able to build an environmental map and
localize itself inside that map. Achieving a solution of this problem allows the exploration of
area that can be dangerous or inaccessible for humans.
In our implementation we decide to use two primary sensors for the environment
exploration: an optical and a thermal camera. Prior work on the combined use of optical and
thermal sensors for the Simultaneous Localization And Mapping (SLAM) problem is limited.
The innovative aspect of this work is based on this combined use of a secondary thermal
camera as an additional visual sensor for navigation under varying environmental conditions.
A secondary innovative aspect is that we focus our attention on both cameras, using them as
two separate and independent sensors and combine the information in the final stage of
environmental mapping.
During the mobile robot navigation the two cameras capture images on the environment
and SURF feature points are extracted and matched between successive scenes. Using a prior
work on bearing-only SLAM approach as a reference, a feature initialization method is
implemented and allows each new good candidate feature (optical or thermal) to be
initialized with a sum of Gaussians that represents a set of possible spatial positions of the
detected feature. Using successive observations, is possible to estimate the environment
coordinates of the feature and adding it to the Extended Kalman Filter (EKF) dynamic state
vector. The EKF state vector contains the information about the position of the 6 degree of
freedom mobile robot and the environmental landmark coordinates. The update of this
information is managed by the EKF algorithm, a statistical method that allows a prediction of
the state vector and it updates based on sensor information available.
The final methodology is tested in indoor and outdoor environments with several different
light conditions and robot trajectories producing results that are robust in terms of noise in the
images and in other sensor data (i.e. encoders and GPS). The use of the thermal camera
improves the number of landmarks detected during the navigation adding useful information
about the explored area.
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Henderson, Caleb Aleksandr. "Identification of Disease Stress in Turfgrass Canopies Using Thermal Imagery and Automated Aerial Image Analysis." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/103621.
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Remote sensing techniques are important for detecting disease within the turfgrass canopy. Herein, we look at two such techniques to assess their viability in detecting and isolating turfgrass diseases. First, thermal imagery is used to detect differences in canopy temperature associated with the onset of brown patch infection in tall fescue. Sixty-four newly seeded stands of tall fescue were arranged in a randomized block design with two runs with eight blocks each containing four inoculum concentrations within a greenhouse. Daily measurements were taken of the canopy and ambient temperature with a thermal camera. After five consecutive days differences were detected in canopy – ambient temperature in both runs (p=0.0015), which continued for the remainder of the experiment. Moreover, analysis of true colour imagery during this time yielded no significant differences between groups. A field study comparing canopy temperature of adjacent symptomatic and asymptomatic tall fescue and creeping bentgrass canopies showed differences as well (p<0.0492). The second project attempted to isolate spring dead spot from aerial imagery of bermudagrass golf course fairways using a Python script. Aerial images from unmanned aerial vehicle flights were collected from four fairways at Nicklaus Course of Bay Creek Resort in Cape Charles, VA. Accuracy of the code was measured by creating buffer zones around code generated points and measuring how many disease centers measured by hand were eclipsed. Accuracies measured as high as 97% while reducing coverage of the fairway by over 30% compared to broadcast applications. Point density maps of the hand and code points also appeared similar. These data provide evidence for new opportunities in remote turfgrass disease detection.Master of Science in Life Sciences
Turfgrasses are ubiquitous, from home lawns to sports fields, where they are used for their durability and aesthetics. Disease within the turfgrass canopy can ruin these aspects of the turfgrass reducing its overall quality. This makes detection and management of disease within the canopy an important part of maintaining turfgrass. Here we look at the effectiveness of imaging techniques in detecting and isolating disease within cool-season and warm-season turfgrasses. We test the capacity for thermal imagery to detect the infection of tall fescue (Festuca arundenacea) with Rhizoctonia solani, the causal agent of brown patch. In greenhouse experiments, differences were detected in normalized canopy temperature between differing inoculation levels at five days post inoculation, and in field conditions we were able to observe differences in canopy temperature between adjacent symptomatic and non-symptomatic stands. We also developed a Python script to automatically identify and record the location of spring dead spot damage within mosaicked images of bermudagrass golf fairways captured via unmanned aerial vehicle. The developed script primarily used Hough transform to mark the circular patches within the fairway and recorded the GPS coordinates of each disease center. When compared to disease incidence maps created manually the script was able to achieve accuracies as high as 97% while reducing coverage of the fairway by over 30% compared to broadcast applications. Point density maps created from points in the code appeared to match those created manually. Both findings have the potential to be used as tools to help turfgrass managers.
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Omar, Duraid Needham-Aldeen. "Monitoring urban patterns using airborne thermal infrared remotely-sensed imagery." Thesis, University of Nottingham, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.240226.
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Boonmee, Marvin. "Land surface temperature and emissivity retrieval from thermal infrared hyperspectral imagery /." Online version of thesis, 2007. http://hdl.handle.net/1850/5868.
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Amin, A. M. "Geometrical analysis and rectification of thermal infrared video frame scanner imagery video frame scanner imagery and its potential applications to topographic mapping." Thesis, University of Glasgow, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.375444.
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Pantaleoni, Eva. "Assessing Coastal Plain Wetland Composition using Advanced Spaceborne Thermal Emission and Reflection Radiometer Imagery." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/28419.
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Establishing wetland gains and losses, delineating wetland boundaries, and determining their vegetative composition are major challenges that can be improved through remote sensing studies. In this study, we used the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) to separate wetlands from uplands in a study of 870 locations on the Virginia Coastal Plain. We used the first five bands from each of two ASTER scenes (6 March 2005 and 16 October 2005), covering the visible to the short-wave infrared region (0.52-2.185υm). We included GIS data layers for soil survey, topography, and presence or absence of water in a logistic regression model that predicted the location of over 78% of the wetlands. While this was slightly less accurate (78% vs. 86%) than current National Wetland Inventory (NWI) aerial photo interpretation procedures of locating wetlands, satellite imagery analysis holds great promise for speeding wetland mapping, lowering costs, and improving update frequency. To estimate wetland vegetation composition classs of the study locations, we generated a Classification and Regression Tree (CART) model and a Multinomial Logistic Regression (logit) model, and compared their accuracy in separating woody wetlands, emergent wetlands and open water. The overall accuracy of the CART model was 73.3%, while the overall accuracy of the logit model was 76.7%. Although the CART producer's accuracy (correct category classification) of the emergent wetlands was higher than the accuracy from the multinomial logit (57.1% vs. 40.7%), we obtained the opposite result for the woody wetland category (68.7% vs. 52.6%). A McNemar test between the two models and NWI maps showed that their accuracies were not statistically different. We conducted a sub-pixel analysis of the ASTER images to establish canopy cover of forested wetlands. The canopy cover ranged from 0 to 225 m2. We used visble-near-infrared ASTER bands, Delta Normalized Difference Vegetation Index, and a Tasselled Cap transformation in an ordinary linear regression (OLS) model. The model achieved an adjusted-R2 of 0.69 and an RMSE of 2.73% when the canopy cover is less than 16%. For higher canopy cover values, the adjusted-R2 was 0.4 and the RMSE was19.79%. Taken together, these findings suggest that satellite remote sensing, in concert with other spatial data, has strong potential for mapping both wetland presence and type.Ph. D.
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Gregory, Simon. "The geometric correction and registration of airborne line-scanned imagery for temporal thermal studies." Thesis, Aston University, 2001. http://publications.aston.ac.uk/14142/.
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This thesis begins by providing a review of techniques for interpreting the thermal response at the earth's surface acquired using remote sensing technology. Historic limitations in the precision with which imagery acquired from airborne platforms can be geometrically corrected and co-registered has meant that relatively little work has been carried out examining the diurnal variation of surface temperature over wide regions. Although emerging remote sensing systems provide the potential to register temporal image data within satisfactory levels of accuracy, this technology is still not widely available and does not address the issue of historic data sets which cannot be rectified using conventional parametric approaches. In overcoming these problems, the second part of this thesis describes the development of an alternative approach for rectifying airborne line-scanned imagery. The underlying assumption that scan lines within the imagery are straight greatly reduces the number of ground control points required to describe the image geometry. Furthermore, the use of pattern matching procedures to identify geometric disparities between raw line-scanned imagery and corresponding aerial photography enables the correction procedure to be almost fully automated. By reconstructing the raw image data on a truly line-by-line basis, it is possible to register the airborne line-scanned imagery to the aerial photography with an average accuracy of better than one pixel. Providing corresponding aerial photography is available, this approach can be applied in the absence of platform altitude information allowing multi-temporal data sets to be corrected and registered.
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Wilbur, Nathan. "Characterizing thermal refugia for brook trout (Salvelinus fontinalis) and Atlantic salmon (Salmo salar) in the Cains River, New Brunswick, Canada." Thesis, Fredericton: University of New Brunswick, 2012. http://hdl.handle.net/1882/35663.
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Anthropogenic influences and climate change are warming rivers in New Brunswick and threatening the cold water habitats of native salmonids. When ambient river
temperatures in summer exceed the tolerance level of Atlantic salmon and brook trout,
individuals behaviourally thermoregulate by seeking out cold water refugia. These
critical thermal habitats are often created by tributaries and concentrated groundwater
discharge. Thermal infrared imagery was used to map cold water anomalies along a 53 km reach of the Cains River on 23 July 2008. Although efficient and useful for mapping surface temperature of a continuous stream reach, the fish did not use all identified thermal anomalies as refugia. Overall, 100 % of observed large brook trout >35 cm in length were found in 30 % of the TIR-mapped cold water anomalies. Ninety eight percent of observed small brook trout 8 – 30 cm in length were found in 80 % of the mapped cold water anomalies and their densities within anomalies were significantly higher than densities outside of anomalies. Fifty nine percent of observed salmon parr were found in 65 % of the mapped anomalies; however, they were dispersed within study sites and their densities were not significantly different within anomalies compared to outside of the anomalies. No brook trout were observed at the seven noncold water study sites that were investigated. Preference curves for various habitat variables including velocity, temperature, depth, substrate, and deep water availability near cold water anomalies were developed based on field investigations during high temperature events (ambient river temperature >21 oC). Combined with thermal imagery, managers can use the physical descriptions of thermal refugia developed here as a tool to help conserve and restore critical thermal refugia for Atlantic salmon and brook trout on the Cains River, and potentially similar river systems.
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Roth, Guy W., and n/a. "Agronomic measurements to validate airborne video imagery for irrigated cotton management." University of Canberra. Resource and Environmental Sciences, 1993. http://erl.canberra.edu.au./public/adt-AUC20050801.124927.
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Water is a major factor limiting cotton production and farmers must aim to
optimise crop water use through timely irrigation scheduling decisions. Airborne
video imagery when calibrated with a low density of ground based observations, offers
the potential for near real time monitoring of crop condition, through sequential
coverages of entire cotton fields. Using commercially available video equipment
mounted on a light aircraft images were acquired of field experiments that were
established in commercial cotton fields to test if the imagery could monitor changes in
crop condition. Ground data collected from these experiments were used to evaluate
green, red, near infrared and thermal band imagery for irrigated crop management.
Prior to acquiring imagery, a ground radiometer study was conducted to
investigate if canopy reflectance changed with the onset of crop water stress. Canopy
reflectance decreased in the near infrared and green bands during the five day period
prior to the crop's normal irrigation date. Red reflectance increased only after the crop
irrigation was due, when the crop was suffering from water stress. The greatest
change in canopy reflectance was in the near infrared region, attributable in part to a
decrease in ground cover caused by canopy architectural changes including leaf
wilting. The results of this experiment were used to select spectral filters for the video cameras.
A range of crop conditions were identified in the imagery including; crop
waterlogging, wheeltrack soil compaction, crop nitrogen status, different varieties,
crop maturity, canopy development, soil moisture status, cotton yield and nutgrass
weeds. Thermal imagery was the most successful for distinguishing differences in the
crop soil moisture status. Near infrared imagery was most closely related to crop
canopy development and is recommended for monitoring crop growth.
Linear relationships were found between spectral responses in the imagery,
crop reflectance (%) and crop temperature measured on the ground. Near infrared
reflectance linearly increased, while spectral responses in the green, red and thermal
bands exhibited an inverse relationship with plant height and ground cover. Imagery
collected early in the season was affected by the soil background. Final lint yield was
related to imagery in the red band. As the soil moisture level declined, crop
temperature increased while reflectance in the green band decreased. To ensure an
accurate relationship between soil moisture and thermal imagery, separate calibration
equations are recommended for different stages in the season.
Green, red and near infrared imagery were affected by the sun angle that
caused one side of the imagery to appear brighter than the other. This problem was
greatest in the green and red bands, but was not evident in the thermal imagery.
Changes in solar radiation and air temperature on some occasions caused greater
variation to the imagery between flights, than changes in crop condition per se.
Therefore, it is not aIways possible to directly determine the soil moisture status from
canopy temperature. Further research is required to correct imagery for environmental
variables such as solar radiation, air temperature and vapour pressure deficit.
Thermal imagery offers many improvements to current irrigation scheduling
techniques including the facilitation of locating more representative ground sampling
points. Thermal imagery also enables cotton fields on a farm to be ranked according to
their soil moisture status. This then provides farmers with a visual picture of the crop
water status across the whole farm, which is not possible using conventional ground
scheduling techniques. At this stage, airborne video imagery will not replace soil
moisture data collected for irrigation scheduling, however offers potential to enhance
irrigation scheduling methods by addressing the problem of crop variability within
cotton fields.
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Meerdink, Susan Kay. "Remote Sensing of Plant Species Using Airborne Hyperspectral Visible-Shortwave Infrared and Thermal Infrared Imagery." Thesis, University of California, Santa Barbara, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=13420575.
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In California, natural vegetation is experiencing an increasing amount of stress due to prolonged droughts, wildfires, insect infestation, and disease. Remote sensing technologies provide a means for monitoring plant species presence and function temporally across landscapes. In this his dissertation, I used hyperspectral visible shortwave infrared (VSWIR), hyperspectral thermal (TIR), and hyperspectral VSWIR + broadband TIR imagery to derive key observations of plant species across a gradient of environmental conditions and time frames. In Chapter 2, I classified plant species using hyperspectral VSWIR imagery from 2013–2015 spring, summer, and fall. Plant species maps had the highest classification accuracy using spectra from a single date (mean kappa 0.80–0.86). The inclusion of spectra from other dates decreased accuracy (mean kappa 0.78–0.83). Leave-one-out analysis emphasized the need to have spectra from the image date in the classification training, otherwise classification accuracy dropped significantly (mean kappa 0.31–0.73). In Chapter 3, I used hyperspectral TIR imagery to determine the extent that high precision spectral emissivity and canopy temperature can be exploited for vegetation research at the canopy level. I found that plant species show distinct spectral separation at the leaf level, but separability among species is lost at the canopy level. However, species’ canopy temperatures exhibited different distributions among dates and species. Variability in canopy temperatures was largely explained by LiDAR derived canopy structural attributes (e.g. canopy density) and the surrounding environment (e.g. presence of pavement). In Chapter 4, I used combined hyperspectral VSWIR and broadband TIR imagery to monitor plant stress during California’s 2013–2015 severe drought. The temperature condition index (TCI) was calculated to measure plant stress by using plant species’ surface minus air temperature distributions across dates. Plant stress was not evenly distributed across the landscape or time with lower elevation open shrub/meadows, showing the largest amount of stress in June 2014, and August 2015 imagery. Plant stress spatial variability across the study area was related to a slope’s aspect with highly stressed plants located on south or south-southwest facing slopes. Overall, this dissertation quantifies the ability to temporally study plant species using hyperspectral VSWIR, hyperspectral TIR, and combined VSWIR+TIR imagery. This analysis supports a range of current and planned missions including Surface Biology and Geology (SBG), Environmental Mapping and Analysis Program (EnMAP), National Ecological Observatory Network (NEON), Hyperspectral Thermal Emission Spectrometer (HyTES), and ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS).
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Bhaskar, Ranjit. "A study of techniques to improve the effective resolution of thematic mapper thermal infrared imagery /." Online version of thesis, 1993. http://hdl.handle.net/1850/11353.
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Bombrun, Maxime. "Characterisation of volcanic emissions through thermal vision." Thesis, Clermont-Ferrand 2, 2015. http://www.theses.fr/2015CLF22600/document.
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En avril 2010, l’éruption de l’Eyjafjallajökull (Islande) a projeté des cendres sur toute l’Europe pendant six jours, causant d’importantes perturbations aériennes. Cette crise a soulevé la nécessité de mieux comprendre la dynamique des panaches lors de l’émission, de la dispersion, et de la retombée afin d’améliorer les modèles de suivis et de prédiction de ces phénomènes. Cette éruption a été classée comme Strombolienne. Ce type d’éruption offre un large panel de manifestations (coulée de lave, paroxysmes) et peut être utilisé comme indicateur d’éruptions plus dangereuses. Les éruptions stromboliennes permettent généralement une observation à quelques centaines de mètres tout en assurant la sécurité des opérateurs et du matériel. Depuis 2001, les caméras thermiques ont été de plus en plus utilisées pour comprendre la dynamique des évènements volcaniques. Toutefois, l’analyse, la modélisation et le post-traitement de ces données thermiques n’est toujours pas totalement informatisé. Durant ma thèse, j’ai étudié les différentes composantes d’une éruption strombolienne depuis les fines particules éjectées au niveau du cratère jusqu’à la vision d’ensemble offerte par les images satellites. Dans l’ensemble, j’ai caractérisé les émissions volcaniques à travers l’imagerie thermiqueIn April 2010, the eruption of Eyjafjallajökull (Iceland) threw volcanic ash across northwest Europe for six days which led to air travel disruption. This recent crisis spotlighted the necessity to parameterise plume dynamics through emission, dispersion and fall out as to better model, track and forecast cloud motions. This eruption was labeled as a Strombolian-to-Sub-Plinian eruption type. Strombolian eruptions are coupled with a large range of volcanic event types (Lava flows, paroxysms) and eruption styles (Hawaiian, Sub-plinian) and offer a partial precursory-indicator of more dangerous eruptions. In addition, strombolian eruptions are small enough to allow observations from within few hundred meters with relative safety, for both operators and material. Since 2001, thermal cameras have been increasingly used to track, parameterise and understand dynamic volcanic events. However, analyses, modelling and post-processing of thermal data are still not fully automated. In this thesis, I focus on the different components of strombolian eruptions at the full range of remote sensing spatial scales. These range from millimeters for particles to kilometers for the entire features via satellite images. Overall, I aim to characterise volcanic emissions through thermal vision
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Scrofani, James William. "An adaptive method for the enhanced fusion of low-light visible and uncooled thermal infrared imagery." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1997. http://handle.dtic.mil/100.2/ADA334031.
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Alkandri, Ahmad. "Design and performance assessment of correlation filters for the detection of objects in high clutter thermal imagery." Thesis, University of Sussex, 2014. http://sro.sussex.ac.uk/id/eprint/49954/.
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The research reported in this thesis has examined means of enhancing the performance of the Optimal Trade-off Maximum Average Correlation Height (OT-MACH) filter for target detection in Forward Looking Infra-Red (FLIR) imagery acquired from a helicopter and border security FLIR camera in northern Kuwait. The data acquired with these FLIR sensors allows real-world evaluation of the comparative performance of the various filters that have been developed in the thesis. The results obtained have been quantified using well known performance measures such as Peak to Side-lobe Ratio (PSR) and Total Detection Error (TDE). The initial focus was to study the effect of modifying the OT-MACH parameters on the correlation metrics. A new optimisation technique has been presented, which computes statistically the filter alpha parameter associated with controlling the response of the filter to clutter noise. A further modification of the OT-MACH filter performance using the Difference of Gaussian bandpass filter (named the D-MACH filter) as a pre-processing stage has been described. The D-MACH has been applied to several test images containing single and multiple targets in the scene. Enhanced performance of the modified filter is demonstrated with improved metrics being obtained with less false side peaks in the correlation plane, especially when multiple targets are present in the test images. A further pre-processing technique was investigated using the Rayleigh distribution as a pre-processing filter (named the R-MACH filter). The R-MACH filter has been applied to multiple target types with tests conducted across various image data sets. The filter demonstrated an improvement over the Difference of Gaussian filter in terms of 6 reducing the number of parameters needing to be tuned whilst producing further enhanced correlation plane metrics. Finally, recommendations for future work has been made to improve the use of the OT-MACH filter in target detection and identification. A novel training image representation is proposed for further investigation, which will minimise the computational intensity of using the MACH filter for unconstrained object recognition.
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Tallman, Jake T. "SOARNET, Deep Learning Thermal Detection For Free Flight." DigitalCommons@CalPoly, 2021. https://digitalcommons.calpoly.edu/theses/2339.
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Thermals are regions of rising hot air formed on the ground through the warming of the surface by the sun. Thermals are commonly used by birds and glider pilots to extend flight duration, increase cross-country distance, and conserve energy. This kind of powerless flight using natural sources of lift is called soaring. Once a thermal is encountered, the pilot flies in circles to keep within the thermal, so gaining altitude before flying off to the next thermal and towards the destination. A single thermal can net a pilot thousands of feet of elevation gain, however estimating thermal locations is not an easy task. Pilots look for different indicators: color variation on the ground because the difference in the amount of heat absorbed by the ground varies based on the color/composition, birds circling in an area gaining lift, and certain types of cloud formations (cumulus clouds). The above methods are not always reliable enough and pilots study the weather for thermals by estimating solar heating of the ground using cloud cover and time of year and the lapse rate and dew point of the troposphere. In this paper, we present a Machine Learning based solution for assisting in forecasting thermals. We created a custom dataset using flight data recorded and uploaded to public databases by soaring pilots. We determine where and when the pilot encountered thermals to pull weather and satellite images corresponding to the location and time of the flight. Using this dataset we train an algorithm to automatically predict the location of thermals given as input the current weather conditions and terrain information obtained from Google Earth Engine and thermal regions encountered as truth labels. We were able to converge very well on the training and validation set, proving our method with around a 0.98 F1 score. These results indicate success in creating a custom dataset and a powerful neural network with the necessity of bolstering our custom dataset.
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Carpenter, Sean A. "A Supervised Machine Learning approach to foliage temperature extraction from UAS imagery in natural environments." The Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1617796342057912.
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Mallat, Khawla. "Efficient integration of thermal technology in facial image processing through interspectral synthesis." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS223.
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La technologie de l'imagerie thermique a largement évolué au cours des deux dernières décennies, grâce aux caméras thermiques qui sont devenues plus abordables et simple à utiliser. Cependant, et étant donné que l'exploration de l'imagerie thermique est relativement nouvelle, seules quelques bases de données publiques sont accessibles à la communauté de recherche. Cette limitation empêche donc l'impact des technologies d'apprentissage profond de générer des systèmes fiables de reconnaissance faciale adaptés au spectre thermique. En essayant de surmonter ces contraintes, les travaux de recherche présentés dans ce manuscrit visent à explorer la synthèse interspectrale pour une intégration efficace et rapide de la technologie thermique dans les systèmes de biométrie faciale existants. Comme première contribution, une nouvelle base de données, contenant des paires d'images de visages visibles et thermiques acquises simultanément, a été collectée et mise en public afin de favoriser la recherche dans le domaine de l’imagerie thermique de visage. Motivé par le besoin d'une intégration simple dans les systèmes de biométrie faciale existants, un ensemble de contributions a proposé un cadre de reconnaissance faciale cross-spectral basé sur une nouvelle approche de synthèse des visages afin d'estimer le visage visible à partir d’un visage thermique. Autres contributions consistant à explorer la synthèse interspectrale, du spectre visible au spectre thermique, pour des tâches de traitement d'images faciales liées à la reconnaissance faciale, sont également présentées notamment la détection des points caractéristiques de visage et l'usurpation d’identité dans le spectre thermiqueThermal imaging technology has significantly evolved during the last couple of decades, mostly thanks to thermal cameras having become more affordable and user friendly. However, and given that the exploration of thermal imagery is reasonably new, only a few public databases are available to the research community. This limitation consequently prevents the impact of deep learning technologies from generating improved and reliable face biometric systems that operate in the thermal spectrum. A possible solution relates to the development of technologies that bridge the gap between visible and thermal spectra. In attempting to respond to this necessity, the research presented in this dissertation aims to explore interspectral synthesis as a direction for efficient and prompt integration of thermal technology in already deployed face biometric systems.As a first contribution, a new database, containing paired visible and thermal face images acquired simultaneously, was collected and made publicly available to foster research in thermal face image processing. Motivated by the need for fast and straightforward integration into existing face recognition systems, a set of contributions consisted in proposing a cross-spectrum face recognition framework based on a novel approach of thermal-to-visible face synthesis in order to estimate the visible face from the thermal input. Contributions consisting in exploring interspectral synthesis from visible to thermal spectrum for facial image processing tasks related to, but different than face recognition, are also presented including facial landmark detection and face biometric spoofing in thermal spectrum
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Sharma, Vinay. "Simultaneous object detection and segmentation using top-down and bottom-up processing." Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1196372113.
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Fernández, Gallego José Armando. "Image processing techniques for plant phenotyping using RGB and thermal imagery = Técnicas de procesamiento de imágenes RGB y térmicas como herramienta para fenotipado de cultivos." Doctoral thesis, Universitat de Barcelona, 2019. http://hdl.handle.net/10803/669111.
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World cereal stocks need to increase in order to meet growing demands. Currently, maize, rice, wheat, are the main crops worldwide, while other cereals such as barley, sorghum, oat or different millets are also well placed in the top list. Crop productivity is affected directly by climate change factors such as heat, drought, floods or storms. Researchers agree that global climate change is having a major impact on crop productivity. In that way, several studies have been focused on climate change scenarios and more specifically abiotic stresses in cereals. For instance, in the case of heat stress, high temperatures between anthesis to grain filling can decrease grain yield. In order to deal with the climate change and future environmental scenarios, plant breeding is one of the main alternatives breeding is even considered to contribute to the larger component of yield growth compared to management. Plant breeding programs are focused on identifying genotypes with high yields and quality to act as a parentals and further the best individuals among the segregating population thus develop new varieties of plants. Breeders use the phenotypic data, plant and crop performance, and genetic information to improve the yield by selection (GxE, with G and E indicating genetic and environmental factors). More factors must be taken into account to increase the yield, such as, for instance, the education of farmers, economic incentives and the use of new technologies (GxExM, with M indicating management). Plant phenotyping is related with the observable (or measurable) characteristics of the plant while the crop growing as well as the association between the plant genetic background and its response to the environment (GxE). In traditional phenotyping the measurements are collated manually, which is tedious, time consuming and prone to subjective errors. Nowadays the technology is involved in many applications. From the point of view of plan phenotyping, technology has been incorporated as a tool. The use of image processing techniques integrating sensors and algorithm processes, is therefore, an alternative to asses automatically (or semi-automatically) these traits. Images have become a useful tool for plant phenotyping because most frequently data from the sensors are processed and analyzed as an image in two (2D) or three (3D) dimensions. An image is the arrangement of pixels in a regular Cartesian coordinates as a matrix, each pixel has a numerical value into the matrix which represents the number of photons captured by the sensor within the exposition time. Therefore, an image is the optical representation of the object illuminated by a radiating source. The main characteristics of images can be defined by the sensor spectral and spatial properties, with the spatial properties of the resulting image also heavily dependent on the sensor platform (which determines the distance from the target object).Las existencias mundiales de cereales deben aumentar para satisfacer la creciente demanda. Actualmente, el maíz, el arroz y el trigo son los principales cultivos a nivel mundial, otros cereales como la cebada, el sorgo y la avena están también bien ubicados en la lista. La productividad de los cultivos se ve afectada directamente por factores del cambio climático como el calor, la sequía, las inundaciones o las tormentas. Los investigadores coinciden en que el cambio climático global está teniendo un gran impacto en la productividad de los cultivos. Es por esto que muchos estudios se han centrado en escenarios de cambio climático y más específicamente en estrés abiótico. Por ejemplo, en el caso de estrés por calor, las altas temperaturas entre antesis y llenado de grano pueden disminuir el rendimiento del grano. Para hacer frente al cambio climático y escenarios ambientales futuros, el mejoramiento de plantas es una de las principales alternativas; incluso se considera que las técnicas de mejoramiento contribuyen en mayor medida al aumento del rendimiento que el manejo del cultivo. Los programas de mejora se centran en identificar genotipos con altos rendimientos y calidad para actuar como progenitores y promover los mejores individuos para desarrollar nuevas variedades de plantas. Los mejoradores utilizan los datos fenotípicos, el desempeño de las plantas y los cultivos, y la información genética para mejorar el rendimiento mediante selección (GxE, donde G y E indican factores genéticos y ambientales). El fenotipado plantas está relacionado con las características observables (o medibles) de la planta mientras crece el cultivo, así como con la asociación entre el fondo genético de la planta y su respuesta al medio ambiente (GxE). En el fenotipado tradicional, las mediciones se clasifican manualmente, lo cual es tedioso, consume mucho tiempo y es propenso a errores subjetivos. Sin embargo, hoy en día la tecnología está involucrada en muchas aplicaciones. Desde el punto de vista del fenotipado de plantas, la tecnología se ha incorporado como una herramienta. El uso de técnicas de procesamiento de imágenes que integran sensores y algoritmos son por lo tanto una alternativa para evaluar automáticamente (o semiautomáticamente) estas características.
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Montanaro, Matthew. "Radiometric modeling of mechanical draft cooling towers to assist in the extraction of their absolute temperature from remote thermal imagery /." Online version of thesis, 2009. http://hdl.handle.net/1850/9667.
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Viau, Claude. "Multispectral Image Analysis for Object Recognition and Classification." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34532.
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Computer and machine vision applications are used in numerous fields to analyze static and dynamic imagery in order to assist or automate some form of decision-making process. Advancements in sensor technologies now make it possible to capture and visualize imagery at various wavelengths (or bands) of the electromagnetic spectrum. Multispectral imaging has countless applications in various field including (but not limited to) security, defense, space, medical, manufacturing and archeology. The development of advanced algorithms to process and extract salient information from the imagery is a critical component of the overall system performance.
The fundamental objectives of this research project were to investigate the benefits of combining imagery from the visual and thermal bands of the electromagnetic spectrum to improve the recognition rates and accuracy of commonly found objects in an office setting. The goal was not to find a new way to “fuse” the visual and thermal images together but rather establish a methodology to extract multispectral descriptors in order to improve a machine vision system’s ability to recognize specific classes of objects.A multispectral dataset (visual and thermal) was captured and features from the visual and thermal images were extracted and used to train support vector machine (SVM) classifiers. The SVM’s class prediction ability was evaluated separately on the visual, thermal and multispectral testing datasets. Commonly used performance metrics were applied to assess the sensitivity, specificity and accuracy of each classifier.
The research demonstrated that the highest recognition rate was achieved by an expert system (multiple classifiers) that combined the expertise of the visual-only classifier, the thermal-only classifier and the combined visual-thermal classifier.
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Mitchell, Monique Tashell. "The Usefulness of Ground Penetrating Radar in locating burials in Charity Hospital Cemetery, New Orleans." ScholarWorks@UNO, 2008. http://scholarworks.uno.edu/td/686.
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The Charity Hospital Cemetery in New Orleans, Louisiana, was used as a potter's field for over 150 years. When Charity Hospital considered selling a portion of the property ground penetrating radar (GPR) and thermal infrared (TIR) data were collected in the cemetery to locate unmarked graves. The TIR data could not be used because the expert died before compiling the TIR data. Therefore, the GPR data was the sole source of subsurface information. GPR anomalies were used to excavate 3 areas where bones and hospital supplies were subsequently found, unfortunately very limited analyses were possible on the analog GPR data. The study presented here involved digitizing data and conducting a more thorough analysis of map patterns to determine whether GPR data could be used reliably to locate burials in the cemetery. The study's result indicates that GPR is a reliable source for burial detection and other anomalies in the subsurface.
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Sudholz, Ashlee. "Machine learning for the automated detection of deer in drone and camera trap imagery." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/212981/1/Ashlee_Sudholz_Thesis.pdf.
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To effectively manage the growing population of invasive deer in Australia, adequate monitoring techniques are essential. Traditional methods of detecting and monitoring deer such as scat surveys, spotlighting, or piloted aerial surveys can be expensive and time consuming. To overcome these issues, camera traps and remotely piloted aircraft systems (RPAS or drones) are increasingly being used to detect and monitor deer populations. This thesis presents a new method for assessing the imagery provided by RPAS and camera traps using Machine Learning, reducing the time and cost of assessing deer populations, providing the opportunity for more efficient management.
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Tian, Bingwei. "Geothermal resource assessment in shallow crust of Japan by three-dimensional temperature modeling using satellite imagery and well-logging dataset." 京都大学 (Kyoto University), 2015. http://hdl.handle.net/2433/199293.
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Agenbag, Johannes Jacobus. "A procedure for the computation of sea surface advection velocities from satellite thermal band imagery, with applications to the South East Atlantic Ocean." Doctoral thesis, University of Cape Town, 1992. http://hdl.handle.net/11427/23143.
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The research was carried out with a view to developing a procedure for the computation of sea surface advection velocities from pairs of NOAA AVHRR infrared images. The procedure was designed for application to the oceanic regions around South Africa and cognisance had to be taken of restrictions imposed by the specific oceanographic conditions, availability of satellite data, as well as the capabilities of the image processing system used. As a first step, a set of image navigation algorithms was developed, based on elliptical orbit and ellipsoidal earth models. Orbit parameters were obtained from TBUS-bulletins and one or more ground reference points had to be identified on each. The navigation algorithms were then used to develop a procedure for the geometric transformation of images to a Mercator map projection. The transformation procedure was evaluated through use of test-images and the results indicated that the maximum errors which could be expected in the computation of advection vectors were 4-5 cm/sin the north/south velocity component and 6-7 cm/sin the east/west component if two images, 12 hours apart in time, were used for the vector computation. An automatic feature tracking method was tested as a means for computing advection velocities but was found to be unsatisfactory. As a result, a 'semi-automated' procedure was developed. This process is essentially a manual (point-wise) feature tracking procedure into which the template matching technique which formed the basis of automated procedures, was incorporated as a labour saving device. Tests indicated a time saving of 20-40 % on the manual procedure and more rapid computation than with the automated procedure. The feature tracking procedure was applied to three sets of AVHRR images of the South East Atlantic. To assess the precision of the vector computation procedure, two independent vector sets were computed. A comparison of the two sets indicated that the rootmean- square deviation in vector magnitude (speed) was about 6-8 cm/sand in the vector direction, about 31° (12° if very small vectors ≤ 6 cm/s are excluded). The computed vectors compared very well with reported results from conventional methods. The derived vector fields also provide the first really detailed description of surface currents in the sea off South Africa: eg. on the flow field in the southern Benguela Current, the circulation associated with Agulhas Current rings, and advective influences on the transport of fish eggs and larvae from the spawning grounds on the Agulhas Bank to the favoured recruitment area off the West Coast.
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Corcoran, Evangeline Mae. "Monitoring and modelling vulnerable wildlife populations using remotely piloted aircraft systems and machine learning." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/212423/1/Evangeline_Corcoran_Thesis.pdf.
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In this thesis a new method for monitoring wildlife using drones and machine learning was developed and used to accurately detect threatened koalas in natural, complex forest habitats for the first time. The new automated detection method was robust, efficient, and was not subject to biases affecting ground surveys or manual analysis of thermal images from drones. A new statistical modelling approach was also developed that allowed accurate estimates of abundance to be made from automated detections of wildlife in drone surveys.
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Denman, Simon Paul. "Improved detection and tracking of objects in surveillance video." Queensland University of Technology, 2009. http://eprints.qut.edu.au/29328/.
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Surveillance networks are typically monitored by a few people, viewing several monitors displaying the camera feeds. It is then very dicult for a human op- erator to eectively detect events as they happen. Recently, computer vision research has begun to address ways to automatically process some of this data, to assist human operators. Object tracking, event recognition, crowd analysis and human identication at a distance are being pursued as a means to aid human operators and improve the security of areas such as transport hubs. The task of object tracking is key to the eective use of more advanced technolo- gies. To recognize an event people and objects must be tracked. Tracking also enhances the performance of tasks such as crowd analysis or human identication. Before an object can be tracked, it must be detected. Motion segmentation tech- niques, widely employed in tracking systems, produce a binary image in which objects can be located. However, these techniques are prone to errors caused by shadows and lighting changes. Detection routines often fail, either due to erro- neous motion caused by noise and lighting eects, or due to the detection routines being unable to split occluded regions into their component objects. Particle l- ters can be used as a self contained tracking system, and make it unnecessary for the task of detection to be carried out separately except for an initial (of- ten manual) detection to initialise the lter. Particle lters use one or more extracted features to evaluate the likelihood of an object existing at a given point each frame. Such systems however do not easily allow for multiple objects to be tracked robustly, and do not explicitly maintain the identity of tracked objects. This dissertation investigates improvements to the performance of object tracking algorithms through improved motion segmentation and the use of a particle lter. A novel hybrid motion segmentation / optical
ow algorithm, capable of simulta- neously extracting multiple layers of foreground and optical
ow in surveillance video frames is proposed. The algorithm is shown to perform well in the presence of adverse lighting conditions, and the optical
ow is capable of extracting a mov- ing object. The proposed algorithm is integrated within a tracking system and evaluated using the ETISEO (Evaluation du Traitement et de lInterpretation de Sequences vidEO - Evaluation for video understanding) database, and signi- cant improvement in detection and tracking performance is demonstrated when compared to a baseline system. A Scalable Condensation Filter (SCF), a particle lter designed to work within an existing tracking system, is also developed. The creation and deletion of modes and maintenance of identity is handled by the underlying tracking system; and the tracking system is able to benet from the improved performance in uncertain conditions arising from occlusion and noise provided by a particle lter. The system is evaluated using the ETISEO database. The dissertation then investigates fusion schemes for multi-spectral tracking sys- tems. Four fusion schemes for combining a thermal and visual colour modality are evaluated using the OTCBVS (Object Tracking and Classication in and Beyond the Visible Spectrum) database. It is shown that a middle fusion scheme yields the best results and demonstrates a signicant improvement in performance when compared to a system using either mode individually. Findings from the thesis contribute to improve the performance of semi- automated video processing and therefore improve security in areas under surveil- lance.
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Denman, Simon P. "Improved detection and tracking of objects in surveillance video." Thesis, Queensland University of Technology, 2009. https://eprints.qut.edu.au/29328/1/Simon_Denman_Thesis.pdf.
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Surveillance networks are typically monitored by a few people, viewing several monitors displaying the camera feeds. It is then very difficult for a human operator to effectively detect events as they happen. Recently, computer vision research has begun to address ways to automatically process some of this data, to assist human operators. Object tracking, event recognition, crowd analysis and human identification at a distance are being pursued as a means to aid human operators and improve the security of areas such as transport hubs. The task of object tracking is key to the effective use of more advanced technologies. To recognize an event people and objects must be tracked. Tracking also enhances the performance of tasks such as crowd analysis or human identification. Before an object can be tracked, it must be detected. Motion segmentation techniques, widely employed in tracking systems, produce a binary image in which objects can be located. However, these techniques are prone to errors caused by shadows and lighting changes. Detection routines often fail, either due to erroneous motion caused by noise and lighting effects, or due to the detection routines being unable to split occluded regions into their component objects. Particle filters can be used as a self contained tracking system, and make it unnecessary for the task of detection to be carried out separately except for an initial (often manual) detection to initialise the filter. Particle filters use one or more extracted features to evaluate the likelihood of an object existing at a given point each frame. Such systems however do not easily allow for multiple objects to be tracked robustly, and do not explicitly maintain the identity of tracked objects. This dissertation investigates improvements to the performance of object tracking algorithms through improved motion segmentation and the use of a particle filter. A novel hybrid motion segmentation / optical flow algorithm, capable of simultaneously extracting multiple layers of foreground and optical flow in surveillance video frames is proposed. The algorithm is shown to perform well in the presence of adverse lighting conditions, and the optical flow is capable of extracting a moving object. The proposed algorithm is integrated within a tracking system and evaluated using the ETISEO (Evaluation du Traitement et de lInterpretation de Sequences vidEO - Evaluation for video understanding) database, and significant improvement in detection and tracking performance is demonstrated when compared to a baseline system. A Scalable Condensation Filter (SCF), a particle filter designed to work within an existing tracking system, is also developed. The creation and deletion of modes and maintenance of identity is handled by the underlying tracking system; and the tracking system is able to benefit from the improved performance in uncertain conditions arising from occlusion and noise provided by a particle filter. The system is evaluated using the ETISEO database. The dissertation then investigates fusion schemes for multi-spectral tracking systems. Four fusion schemes for combining a thermal and visual colour modality are evaluated using the OTCBVS (Object Tracking and Classification in and Beyond the Visible Spectrum) database. It is shown that a middle fusion scheme yields the best results and demonstrates a significant improvement in performance when compared to a system using either mode individually. Findings from the thesis contribute to improve the performance of semi-automated video processing and therefore improve security in areas under surveillance.
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Zhyzha, M., Алла Миколаївна Дядечко, Алла Николаевна Дядечко, and Alla Mykolaivna Diadechko. "Thermal imagers." Thesis, Сумський державний університет, 2013. http://essuir.sumdu.edu.ua/handle/123456789/31088.
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There is no need to remind of the importance of a technical process in our lives. Humanity has already accustomed to the modern devices and equipment and we cannot imagine how we lived without it. The development of technology finds applications in life, medicine, education, industry and business. But one has to remember that each device may have both positive and negative impact on human health.
When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/31088
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Bharadwaj, Akshay S. "A Perception Payload for Small-UAS Navigation in Structured Environments." Ohio University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1533649419108963.
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Srinivas, Umamahesh Bose N. K. "Thermal image superresolution and higher order whitening of images." [University Park, Pa.] : Pennsylvania State University, 2009. http://etda.libraries.psu.edu/theses/approved/WorldWideIndex/ETD-3979/index.html.
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Acuña, Paz y. Miño Jairo. "Application des méthodes d’imagerie au rayonnement dans les scènes urbaines." Thesis, Pau, 2020. http://www.theses.fr/2020PAUU3023.
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Ce travail repose sur les techniques d’imagerie photographique et thermographique qui permettent de mieux comprendre les échanges radiatifs d’une scène urbaine en donnant des résultats visuels et quantitatifs. Deux types d’image sont construits et exploités : des panoramas sphériques, couvrant un angle solide de 4π stéradians, et des perspectives urbaines.Les études de scènes urbaines effectuées dans des conditions différentes, Bayonne en hiver et Cordoue en été, sont présentées à travers deux applications. La première est centrée sur le confort thermique urbain et la seconde sur les échanges thermiques entre les surfaces d’une rue.Dans la première application, l’étude se concentre sur le rayonnement incident en un point provenant de toutes les directions. Le rayonnement est mesuré dans les spectres visible et infrarouge, puis extrapolé à l’ensemble du spectre. L'image 4π qui en résulte représente la distribution spatiale du rayonnement et sert à calculer la température moyenne de rayonnement. Ce résultat est validé par des comparaisons avec les mesures utilisant le globe noir, considéré comme la méthode de référence. La méthode 4π offre une mesure rapide, indépendante des effets de convection, avec une discrimination spectrale en deux bandes. Elle sert à jauger une ville à partir de peu de points de mesure, mais aussi à obtenir des informations spatialisées précises. Ainsi, l'étude réalisée à Cordoue montre que la rugosité du sol dans deux scènes analysées dans des conditions similaires influence le confort thermique du piéton.Pour la deuxième application, l'étude est concentrée sur les températures de surface. Un modèle 3D simplifié est combiné à du lancer de rayons pour corriger la thermographie en filtrant les flux parasites. Le résultat est validé sur une rue en perspective par comparaison avec le résultat de plusieurs thermomètres de contact. Cette comparaison, qui ne peut être que ponctuelle et limitée à certaines surfaces, donne des résultats satisfaisants qui permettent d'étendre la correction à l'ensemble de l'image.Les thermographies corrigées révèlent des phénomènes qui n'étaient pas visibles auparavant, les écarts de température des fenêtres étant les plus remarquables. En effet, le verre, matériau à haute transmittance thermique, met en évidence d’importantes pertes d'énergie lors de sa traversée. Ces pertes sont particulièrement visibles lors d'une froide journée d'hiver, lorsque le chauffage est en fonctionnement. Dans une rue de type canyon, l'étude qualitative du rayonnement séparément dans deux bandes spectrales montre l'intérêt d'en inclure une troisième pour mieux comprendre le passage de la réflexion diffuse à la réflexion spéculaire. Cette bande serait celle de l'infrarouge proche. Les deux applications apportent des connaissances fondamentales sur le paysage radiatif de la ville et montrent bien la relation entre la géométrie et le rayonnement. Ce n'est qu'à travers cette relation qu'il est possible d'identifier les phénomènes physiques qui doivent être pris en compte pour la simulation thermique urbaineThis work relies on imagery, photographic and thermographic, allowing a better understanding of the radiative exchanges of an urban scene by giving both visual and quantitative results. Two types of images are constructed and exploited: spherical panoramas, referred to as 4π as they cover a solid angle of 4π steradians, and urban perspectives.The study of urban scenes in different places and conditions, Bayonne in winter and Cordoba in summer, are presented through two applications. The first focuses on urban thermal comfort and the second on heat exchanges between the surfaces of a street.In the first application, the study focuses on the incident radiation coming from all directions at a single point. The radiation is measured in both the visible and infrared range and then extrapolated to the whole spectrum. The resulting image 4π represents the radiative spatial distribution and is used to calculate the mean radiant temperature. This result is validated by comparisons with measurements performed using the black globe, accepted as the reference method. The 4π method offers a fast, convection-independent measurement with spectral discrimination in two bands. It serves to evaluate a city from few measurement points, but also to obtain precise spatialized information. The study carried out in Cordoba shows that the roughness of the ground in two scenes evaluated under similar conditions influences the thermal comfort of the pedestrian.For the second application, the study focuses on surface temperatures. We use a simplified 3D model combined with ray tracing to correct the thermography by filtering parasitic fluxes. The result is validated on a perspective street by comparison with the result of several contact thermometers. This comparison, which can only be punctual and limited to certain surfaces, gives satisfactory results that allow us to extend the correction to the entire image.A corrected thermogram reveals phenomena that were formerly not visible, the temperature difference of the windows being the most remarkable. Indeed, glass, a material with high heat transmission, exhibits the heat loss that occurs through its surface. This is particularly noticeable on a cold winter's day when the heating is in operation. In a canyon-type street, the qualitative study of the radiation discriminated in two spectral bands shows the interest of including a third one, that of the near infrared, to better understand the passage from diffuse to specular reflection. These two applications provide fundamental knowledge on the radiative landscape of the city and show the relationship between geometry and radiation. Only through this relationship is it possible to identify the physical phenomena that must be accounted in an urban thermal simulation
Este trabajo se basa en el uso de la imagen: fotográfica y termográfica. Esta permite una mejor comprensión de los intercambios radiativos de la escena urbana al proporcionar un resultado tanto visual como cuantitativo mediante la construcción y manipulación de dos tipos de imágenes: las panorámicas esféricas, llamadas 4π porque cubren un ángulo sólido de 4π estereorradianes, y las perspectivas urbanas.El estudio de escenas urbanas bajo diferentes condiciones ambientales, Bayona en invierno y Córdoba en verano, se presenta a través de dos aplicaciones. La primera se centra en el confort térmico urbano y la segunda en los intercambios térmicos entre las superficies de una calle.En la primera aplicación, el estudio se enfoca en la radiación incidente en un punto proveniente de todas las direcciones. La radiación se mide en dos rangos del espectro, el visible y el infrarrojo, y se extrapola a todo el espectro. La imagen 4π resultante representa la distribución espacial de la radiación y se utiliza para calcular la temperatura media radiante. Este resultado se valida mediante comparaciones con mediciones utilizando el globo negro, considerado como método de referencia. El método 4π ofrece una medición rápida e independiente de la convección con una discriminación espectral en dos bandas. Ésta sirve para evaluar una ciudad a partir de pocos puntos de medición, pero también para obtener información espacializada precisa. El estudio realizado en Córdoba muestra que la rugosidad del suelo en dos escenas evaluadas en condiciones similares influye en el confort térmico del peatón.En la segunda aplicación, el estudio se concentra en las temperaturas de la superficie. Nos servimos de un modelo 3D simplificado en combinación con el trazado de rayos para corregir la termografía filtrando los flujos parásitos. El resultado se valida en la perspectiva de una calle comparándolo con el resultado de varios termómetros de contacto. Esta comparación, que sólo puede ser puntual y limitada a ciertas superficies, da resultados satisfactorios que permiten extender la corrección a toda la imagen.Una termografía corregida revela fenómenos que antes no eran visibles, destacándose la diferencia de temperatura de las ventanas. Efectivamente, el vidrio, un material con una alta transmitancia térmica, pone en evidencia la energía que se pierde a través de su superficie. Esto es particularmente visible en un día frío de invierno, cuando la calefacción está en funcionamiento. En el caso de una calle tipo cañón, el estudio cualitativo de la radiación discriminada en dos bandas espectrales muestra el interés de incluir una tercera para comprender mejor el paso de la reflexión difusa a la especular, la del infrarrojo cercano. Las dos aplicaciones proporcionan conocimientos fundamentales sobre el paisaje radiativo de la ciudad y muestran el vínculo entre la geometría y la radiación. Es sólo a través de esta relación que es posible identificar los fenómenos físicos que deben ser tenidos en cuenta para una simulación térmica urbana
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Quek, Yew Sing. "Characterization of 3-5 micron thermal imagers and analysis of narrow band images." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Dec%5FQuek.pdf.
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Thesis (M.S. in Combat Systems Technology)--Naval Postgraduate School, December 2004.Thesis advisor(s): Alfred W.Cooper, Gamani Karunasiri. Includes bibliographical references (p. 91-92). Also available online.
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Sayão, Veridiana Maria. "Land surface temperature and reflectance spectra integration obtained from Landsat on the soil attributes quantification." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/11/11140/tde-20032018-112133/.
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Soil attributes directly influence on its surface temperature. Although there are several studies using soil spectra obtained from satellites, soil evaluation through Land Surface Temperature (LST) is still scarce. The broad availability of satellite thermal data and the development of algorithms to retrieve LST facilitated its use in soil studies. The objective of this study was to evaluate soil LST variations due to its composition and verify the potential of using LST on soil attributes quantification, also integrated with reflectance spectra and elevation data. The study area (198 ha) is located in Sao Paulo state, Brazil, and had plowed bare soil during the satellite image acquisition date. Soil samples were collected in a regular grid of 100 x 100 m (depths: 0-0.2 m and 0.8- 1.0 m); soil granulometry, organic matter (OM) and iron oxides were determined by wet chemistry analysis. In this study, an image of Landsat 5 was used for extracting LST using the inversion of Planck\'s function in band 6 (10,400 - 12,500 nm), and land surface emissivity was estimated using Normalized Difference Vegetation Index threshold method. Reflectance values were extracted from bands 1, 2, 3, 4, 5 and 7. Models for soil attributes quantification were performed using Linear Regression (LR), with samples from 62 auger points distributed in 14 toposequences. Simple LR was applied for generating prediction models based on LST and on elevation data (extracted from a Digital Elevation Model). Multiple LR was applied in order to generate prediction models using atmospherically corrected spectral reflectance from Visible, Near-Infrared and Shortwave infrared (Vis-NIR-SWIR) bands as predictors, and also for the prediction of soil attributes using simultaneously Vis-NIR-SWIR, LST and elevation data, and only significant variables identified by T-tests were used. Predictive performance of models was assessed based on adjusted coefficient of determination (R2adj), Root Mean Squared Error (RMSE, g kg-1) and Ratio of Performance to Interquartile Range (RPIQ) obtained in validation. Ordinary kriging was also performed and the resulted interpolated surfaces were compared to the maps obtained from the best LR model. There was significant correlation between soil attributes and reflectance, LST and elevation data, and soils with clay texture were differentiated from sandy soils based on LST mean values. For all soil attributes, models using only elevation presented the worst performance; models using only LST, moderate performance; and using Vis-NIR-SWIR bands, good predictive performance. For clay, the best model obtained had bands 4-7, LST and elevation as predictors; for sand and iron oxides, the best model had bands 4-7 and LST; for OM, band 4, band 7 and LST. The use of LST for estimating soil attributes increases the predictive performance of multiple LR models when associated with other variables obtained through remote sensing, particularly surface reflectance data, improving the validation of models reaching high R2adj, high RPIQ and low RMSE values. Maps for sand, OM and iron oxides obtained through ordinary kriging outperformed those obtained for the same attributes using LR models based on RS co-variables, and for clay, both approaches reached the same accuracy level. Mapping of soil clay, sand, OM and iron oxides contents through multiple LR models using Landsat 5 products is a simple and easy to reproduce technique, appropriate for soil attributes mapping in bare soil agricultural areas.Os atributos do solo influenciam diretamente na sua temperatura de superfície. Apesar de existir vários estudos utilizando espectros de solos obtidos de satélite, a avaliação do solo por meio da Temperatura de Superfície Terrestre (em inglês Land Surface Temperature, LST) ainda é escassa. A ampla disponibilidade de dados termais de satélite e o desenvolvimento de algoritmos para derivar a LST facilitou o seu uso em estudos de solos. O objetivo desse trabalho foi avaliar variações da LST do solo devidas à sua composição e verificar o potencial de uso da LST na quantificação de atributos do solo, também integrada com dados de espectros de reflectância e elevação. A área de estudo (198 ha) está localizada no estado de São Paulo, Brasil, e estava com solo exposto e arado na data de aquisição da imagem de satélite. Amostras de solo foram coletadas em um grid regular de 100 x 100 m (profundidades: 0.02 m e 0.8-1.0 m); a granulometria do solo, matéria orgânica (MO) e óxidos de ferro foram determinados via análises físicas e químicas laboratoriais. Neste estudo, uma imagem do Landsat 5 foi utilizada para extrair a temperatura de superfície usando a inversão da função da Lei de Planck na banda 6 (10.400 - 12.500 nm), e a emissividade de superfície foi estimada utilizando o método do limiar do Índice de Vegetação da Diferença Normalizada. Valores de reflectância das bandas 1, 2, 3, 4, 5 e 7 foram extraídos. Modelos para quantificação de atributos do solo foram feitos usando Regressão Linear (RL), com amostras de 62 pontos de tradagem distribuídos em 14 topossequências. A RL simples foi aplicada para gerar modelos de predição baseados na LST e também na elevação (extraída de um modelo digital de elevação). A RL múltipla foi aplicada para gerar modelos de predição usando os espectros de reflectância com correção atmosférica das bandas do Visível, Infravermelho próximo e Infravermelho de ondas curtas (Vis-NIR-SWIR) como preditores; também foi aplicada para predição de atributos do solo usando simultaneamente dados do Vis-NIR-SWIR, LST e elevação, e apenas variáveis significativas identificadas por teste T foram usadas. A performance preditiva dos modelos foi avaliada baseada no coeficiente de determinação ajustado (R2adj), raiz do erro quadrático médio (RMSE, g kg-1) e razão de desempenho do intervalo interquartil (RPIQ) obtidos na validação. A krigagem ordinária também foi feita e as superfícies interpoladas resultantes foram comparadas com o melhor modelo de RL. Houve correlação significativa entre os atributos do solo e dados de reflectância, LST e elevação, e solos com textura argilosa foram diferenciados de solos arenosos com base em valores médios de LST. Para todos os atributos do solo, os modelos usando apenas elevação apresentaram a pior performance, modelos usando somente LST, performance moderada, e usando as bandas do Vis-NIR-SWIR, boa performance preditiva. Para argila, o melhor modelo obtido teve as bandas 4-7, LST e elevação como preditores; para areia e óxidos de ferro, o melhor modelo teve as bandas 4-7 e LST; para MO, banda 4, banda 7 e LST. O uso da LST para estimar atributos do solo aumenta a performance preditiva de modelos de RL múltipla quando associada a outras variáveis obtidas via sensoriamento remoto (SR), particularmente dados de reflectância de superfície, melhorando a validação dos modelos atingindo altos valores de R2adj e RPIQ e baixos valores de RMSE. Os mapas para areia, MO e óxidos de ferro obtidos via krigagem ordinária superaram aqueles obtidos para os mesmos atributos usando modelos de RL baseados em co-variáveis obtidas via SR, e para argila, ambas abordagens atingiram o mesmo nível de acurácia. O mapeamento dos conteúdos de argila, areia, matéria orgânica e óxidos de ferro do solo via modelos de RL múltipla utilizando produtos do Landsat 5 é uma técnica simples e fácil de reproduzir, apropriada para o mapeamento de atributos do solo em áreas de agricultura com solo exposto.
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Moyer, Steven K. "Modeling challenges of advanced thermal imagers." Diss., Available online, Georgia Institute of Technology, 2006, 2006. http://etd.gatech.edu/theses/available/etd-02272006-144729/.
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Thesis (Ph. D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2007.Dr. William T. Rhodes, Committee Co-Chair ; Dr. John Buck, Committee Member ; Dr. William Hunt, Committee Member ; Dr. Stephen P. DeWeerth, Committee Member ; Dr. Ronald G. Driggers, Committee Member ; Dr. Gisele Bennett, Committee Chair.
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Ibrahim, Hassan. "Improved designs for future thermal imagers." Thesis, University of Reading, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.312534.
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Vilcahuaman, Cajacuri Luis Alberto. "Early diagnostic of diabetic foot using thermal images." Phd thesis, Université d'Orléans, 2013. http://tel.archives-ouvertes.fr/tel-01022921.
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The object of the thesis is to analyze the potential of thermography in the early diagnosis of type 2 diabetic foot. The main advantages of thermography are that it is simple to use, non-invasive, contactless, non-irradiant, and fast. A robust acquisition protocol is proposed, as well as a dedicated image processing algorithm. The algorithm includes a pre-processing step, plus a segmentation and a rigid registration procedures. Various parameters are assessed: the mean and standard deviation of right and left feet plantar surfaces temperaure, as well as the percentage of pixels such that the absolute point to point temperature difference between right and left feet is greater than 2.2°C. A percentage greater than 1% indicates significative hyperthermia regions. A transversal clinical study is conducted on a population of 85 persons of type 2 diabetic foot. They are classified in one of these three groups: Low risk, Medium risk, and High risk. For the Low risk group, the mean temperature is close to 32°C. For the medium one, it goes down to 31°C, and increases for the High risk group to a value of 32°C. In the early stage of diabetic foot, i.e. from the Low risk group to Medium risk group, the plantar foot surface temperature is lowered by 1°C: if this result is confirmed by other clinical tests, this information can be useful for the early diagnosis of diabetic foot. Finally, 9 images out of the 85 show hyperthermia, mainly in the heel or toes regions. This hyperthermia indication may be of a substantial assistance in the early prevention of foot ulcer and can help in avoiding subsequent foot amputation.
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Alshatshati, Salahaldin Faraj. "Estimating Envelope Thermal Characteristics from Single Point in Time Thermal Images." University of Dayton / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1512648630005333.
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Rotava, Lucas. "Algoritmos de tempo real para melhoramento de imagens capturadas no espectro do infravermelho projetados para síntese em FPGA." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/18/18152/tde-21012016-143940/.
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Este trabalho apresenta o desenvolvimento de algoritmos de processamento de imagens para câmeras térmicas, com o objetivo de sintetizá-los em FPGA. Existem diversas aplicações para imagens térmicas nas áreas médica, de segurança e industrial, por isso o conhecimento e o desenvolvimento de câmeras térmicas são de interesse para a academia e para a indústria. Por consequência, o desenvolvimento de algoritmos que tratem as imagens também representa importante papel. Os algoritmos implementados neste trabalho são: correção de não uniformidade (NUC); substituição de pixels defeituosos, ou bad pixels, (BPR); redução da resolução de cor com realce de contraste; e filtro espacial para realçar detalhes da imagem, chamado de filtro de nitidez. Os três primeiros são algoritmos importantes devido à características dos detectores e de câmeras térmicas, já o filtro de nitidez foi proposto para melhorar a visualização de objetos nas imagens. Com os algoritmos simulados em Matlab foram feitas medidas de contraste e de MTF das imagens de saída, e os resultados obtidos para os algoritmos de realce de contraste e de nitidez mostraram que eles são adições importantes ao conjunto de algoritmos básicos para câmeras térmicas, já que, para alguns casos, o realce de contraste aumentou em mais de 50% a medida de contraste da imagem, em comparação com o algoritmo anterior, e o filtro de nitidez proporcionou valores de MTF até duas vezes maiores. Os algoritmos de NUC e BPR apresentaram os resultados esperados, corrigindo a imagem recebida do detector. As imagens utilizadas eram de 640×512 pixels processadas em uma taxa de 30 fps, e dessa forma optou-se pelo FPGA para a síntese dos algoritmos, sendo possível realizar os processamentos paralelamente contando com a característica de alto throughput inerente a estes componentes. Os algoritmos implementados em FPGA apresentaram desempenho superior aos requisitos mínimos de tempo para o sistema utilizado, sendo perfeitamente capazes de processar o vídeo de entrada em tempo real.This work presents the development of FPGA-synthesizable image processing algorithms to thermal cameras. There are plenty of applications for thermal imaging in medical, security and industrial areas, therefore, the knowledge and the development of thermal cameras are of great interest to both academia and industry. Consequently, the development of algorithms to enhance the images is also important. The implemented algorithms are: nonuniformity correction (NUC); bad pixel replacement (BPR); pixel depth reduction with contrast enhancement; and emboss spatial filter. The three first algorithms are important because of some characteristics of infrared detectors and cameras, and the emboss filter is proposed to improve the visualization of objects in the images. With the algorithms simulated in Matlab, the contrast and MTF were measured in the output images, and the results showed that the contrast enhancement and the emboss filter algorithms are important additions to the infrared cameras basic set of image processing algorithms since, for some cases, the contrast enhancement was able to improve the contrast by 50% and the emboss filter have doubled the MTF. NUC and BPR algorithms had the expected results, correcting the image from the detector. There were used images with resolution of 640×512 at 30 frames per second and, because of this, it was chosen to synthesize the algorithms in an FPGA, this way it is possible to run them in parallel, counting on the high throughput characteristic of the FPGAs. The implemented algorithms have better timing performance than the needed for the system used, being perfectly able to process the input video in real time.
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Spampinato, Letizia. "Thermal monitoring of active volcanoes using portable infrared imagers." Thesis, University of Cambridge, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609816.
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Cragun, Rebecca. "Thermal microactuators for microelectromechanical systems /." Diss., CLICK HERE for online access, 1999. http://contentdm.lib.byu.edu/ETD/image/etd170.pdf.
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Nyberg, Adam. "Transforming Thermal Images to Visible Spectrum Images Using Deep Learning." Thesis, Linköpings universitet, Datorseende, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-151982.
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Thermal spectrum cameras are gaining interest in many applications due to their long wavelength which allows them to operate under low light and harsh weather conditions. One disadvantage of thermal cameras is their limited visual interpretability for humans, which limits the scope of their applications. In this thesis, we try to address this problem by investigating the possibility of transforming thermal infrared (TIR) images to perceptually realistic visible spectrum (VIS) images by using Convolutional Neural Networks (CNNs). Existing state-of-the-art colorization CNNs fail to provide the desired output as they were trained to map grayscale VIS images to color VIS images. Instead, we utilize an auto-encoder architecture to perform cross-spectral transformation between TIR and VIS images. This architecture was shown to quantitatively perform very well on the problem while producing perceptually realistic images. We show that the quantitative differences are insignificant when training this architecture using different color spaces, while there exist clear qualitative differences depending on the choice of color space. Finally, we found that a CNN trained from daytime examples generalizes well on tests from night time.