Relevant bibliographies by topics / Thermal imagery

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Thermal imagery'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 May 2024

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Thermal imagery.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Thermal imagery"

1

Ulhaq, Anwaar, Peter Adams, Tarnya E. Cox, Asim Khan, Tom Low, and Manoranjan Paul. "Automated Detection of Animals in Low-Resolution Airborne Thermal Imagery." Remote Sensing 13, no. 16 (August 19, 2021): 3276. http://dx.doi.org/10.3390/rs13163276.

Full text
Abstract:
Detecting animals to estimate abundance can be difficult, particularly when the habitat is dense or the target animals are fossorial. The recent surge in the use of thermal imagers in ecology and their use in animal detections can increase the accuracy of population estimates and improve the subsequent implementation of management programs. However, the use of thermal imagers results in many hours of captured flight videos which require manual review for confirmation of species detection and identification. Therefore, the perceived cost and efficiency trade-off often restricts the use of these systems. Additionally, for many off-the-shelf systems, the exported imagery can be quite low resolution (<9 Hz), increasing the difficulty of using automated detections algorithms to streamline the review process. This paper presents an animal species detection system that utilises the cost-effectiveness of these lower resolution thermal imagers while harnessing the power of transfer learning and an enhanced small object detection algorithm. We have proposed a distant object detection algorithm named Distant-YOLO (D-YOLO) that utilises YOLO (You Only Look Once) and improves its training and structure for the automated detection of target objects in thermal imagery. We trained our system on thermal imaging data of rabbits, their active warrens, feral pigs, and kangaroos collected by thermal imaging researchers in New South Wales and Western Australia. This work will enhance the visual analysis of animal species while performing well on low, medium and high-resolution thermal imagery.
APA, Harvard, Vancouver, ISO, and other styles
2

Gao, Lyuzhou, Liqin Cao, Yanfei Zhong, and Zhaoyang Jia. "Field-Based High-Quality Emissivity Spectra Measurement Using a Fourier Transform Thermal Infrared Hyperspectral Imager." Remote Sensing 13, no. 21 (November 5, 2021): 4453. http://dx.doi.org/10.3390/rs13214453.

Full text
Abstract:
Emissivity information derived from thermal infrared (TIR) hyperspectral imagery has the advantages of both high spatial and spectral resolutions, which facilitate the detection and identification of the subtle spectral features of ground targets. Despite the emergence of several different TIR hyperspectral imagers, there are still no universal spectral emissivity measurement standards for TIR hyperspectral imagers in the field. In this paper, we address the problems encountered when measuring emissivity spectra in the field and propose a practical data acquisition and processing framework for a Fourier transform (FT) TIR hyperspectral imager—the Hyper-Cam LW—to obtain high-quality emissivity spectra in the field. This framework consists of three main parts. (1) The performance of the Hyper-Cam LW sensor was evaluated in terms of the radiometric calibration and measurement noise, and a data acquisition procedure was carried out to obtain the useful TIR hyperspectral imagery in the field. (2) The data quality of the original TIR hyperspectral imagery was improved through preprocessing operations, including band selection, denoising, and background radiance correction. A spatial denoising method was also introduced to preserve the atmospheric radiance features in the spectra. (3) Three representative temperature-emissivity separation (TES) algorithms were evaluated and compared based on the Hyper-Cam LW TIR hyperspectral imagery, and the optimal TES algorithm was adopted to determine the final spectral emissivity. These algorithms are the iterative spectrally smooth temperature and emissivity separation (ISSTES) algorithm, the improved Advanced Spaceborne Thermal Emission and Reflection Radiometer temperature and emissivity separation (ASTER-TES) algorithm, and the Fast Line-of-sight Atmospheric Analysis of Hypercubes-IR (FLAASH-IR) algorithm. The emissivity results from these different methods were compared to the reference spectra measured by a Model 102F spectrometer. The experimental results indicated that the retrieved emissivity spectra from the ISSTES algorithm were more accurate than the spectra retrieved by the other methods on the same Hyper-Cam LW field data and had close consistency with the reference spectra obtained from the Model 102F spectrometer. The root-mean-square error (RMSE) between the retrieved emissivity and the standard spectra was 0.0086, and the spectral angle error was 0.0093.
APA, Harvard, Vancouver, ISO, and other styles
3

Galbraith, A. E., J. Theiler, K. J. Thome, and R. W. Ziolkowski. "Resolution enhancement of multilook imagery for the multispectral thermal imager." IEEE Transactions on Geoscience and Remote Sensing 43, no. 9 (September 2005): 1964–77. http://dx.doi.org/10.1109/tgrs.2005.853569.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Singh Rawat, Kishan, V. K. Sehgal, and S. S. Ray. "Downscaling of MODIS thermal imagery." Egyptian Journal of Remote Sensing and Space Science 22, no. 1 (April 2019): 49–58. http://dx.doi.org/10.1016/j.ejrs.2018.01.001.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Wynne, J. Judson, Jeff Jenness, Derek L. Sonderegger, Timothy N. Titus, Murzy D. Jhabvala, and Nathalie A. Cabrol. "Advancing Cave Detection Using Terrain Analysis and Thermal Imagery." Remote Sensing 13, no. 18 (September 8, 2021): 3578. http://dx.doi.org/10.3390/rs13183578.

Full text
Abstract:
Since the initial experiments nearly 50 years ago, techniques for detecting caves using airborne and spacecraft acquired thermal imagery have improved markedly. These advances are largely due to a combination of higher instrument sensitivity, modern computing systems, and processor-intensive analytical techniques. Through applying these advancements, our goals were to: (1) Determine the efficacy of methods designed for terrain analysis and applied to thermal imagery; (2) evaluate the usefulness of predawn and midday imagery for detecting caves; and (3) ascertain which imagery type (predawn, midday, or the difference between those two times) was most informative. Using forward stepwise logistic (FSL) and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses for model selection, and a thermal imagery dataset acquired from the Mojave Desert, California, we examined the efficacy of three well-known terrain descriptors (i.e., slope, topographic position index (TPI), and curvature) on thermal imagery for cave detection. We also included the actual, untransformed thermal DN values (hereafter “unenhanced thermal”) as a fourth dataset. Thereafter, we compared the thermal signatures of known cave entrances to all non-cave surface locations. We determined these terrain-based analytical methods, which described the “shape” of the thermal landscape, hold significant promise for cave detection. All imagery types produced similar results. Down-selected covariates per imagery type, based upon the FSL models, were: Predawn— slope, TPI, curvature at 0 m from cave entrance, as well as slope at 1 m from cave entrance; midday— slope, TPI, and unenhanced thermal at 0 m from cave entrance; and difference— TPI and slope at 0 m from cave entrance, as well as unenhanced thermal and TPI at 3.5 m from cave entrance. We provide recommendations for future research directions in terrestrial and planetary cave detection using thermal imagery.
APA, Harvard, Vancouver, ISO, and other styles
6

Maguire, Mitchell S., Christopher M. U. Neale, and Wayne E. Woldt. "Improving Accuracy of Unmanned Aerial System Thermal Infrared Remote Sensing for Use in Energy Balance Models in Agriculture Applications." Remote Sensing 13, no. 9 (April 22, 2021): 1635. http://dx.doi.org/10.3390/rs13091635.

Full text
Abstract:
Unmanned aerial system (UAS) remote sensing has rapidly expanded in recent years, leading to the development of several multispectral and thermal infrared sensors suitable for UAS integration. Remotely sensed thermal infrared imagery has been used to detect crop water stress and manage irrigation by leveraging the increased thermal signatures of water stressed plants. Thermal infrared cameras suitable for UAS remote sensing are often uncooled microbolometers. This type of thermal camera is subject to inaccuracies not typically present in cooled thermal cameras. In addition, atmospheric interference also may present inaccuracies in measuring surface temperature. In this study, a UAS with integrated FLIR Duo Pro R (FDPR) thermal camera was used to collect thermal imagery over a maize and soybean field that contained twelve infrared thermometers (IRT) that measured surface temperature. Surface temperature measurements from the UAS FDPR thermal imagery and field IRTs corrected for emissivity and atmospheric interference were compared to determine accuracy of the FDPR thermal imagery. The comparison of the atmospheric interference corrected UAS FDPR and IRT surface temperature measurements yielded a RMSE of 2.24 degree Celsius and a R2 of 0.85. Additional approaches for correcting UAS FDPR thermal imagery explored linear, second order polynomial and artificial neural network models. These models simplified the process of correcting UAS FDPR thermal imagery. All three models performed well, with the linear model yielding a RMSE of 1.27 degree Celsius and a R2 of 0.93. Laboratory experiments also were completed to test the measurement stability of the FDPR thermal camera over time. These experiments found that the thermal camera required a warm-up period to achieve stability in thermal measurements, with increased warm-up duration likely improving accuracy of thermal measurements.
APA, Harvard, Vancouver, ISO, and other styles
7

LEINONEN, I., O. M. GRANT, C. P. P. TAGLIAVIA, M. M. CHAVES, and H. G. JONES. "Estimating stomatal conductance with thermal imagery." Plant, Cell and Environment 29, no. 8 (August 2006): 1508–18. http://dx.doi.org/10.1111/j.1365-3040.2006.01528.x.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Ding, De Hong, Kui Fang, He Xiang Yu, Ke Jun Qian, and Dai Jun Cui. "Research of Infrared Thermal Imagery Segmentation Technology Based on Visible Light Image." Applied Mechanics and Materials 401-403 (September 2013): 1534–38. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1534.

Full text
Abstract:
To solve the problem of infrared target recognition, byusing the complementarity of visible-light image and infrared-thermal imagery, thisarticle presents a kind of infrared thermal imagery segmentation technology. Segmentingthe target edges of visible-light images, and superimposing the edge on thecorresponding infrared thermal imagery, then segmenting the infrared thermalimagery by the improved weighted regions growing algorithm. After the testabout relevant parameters of the infraredthermal imagery, found that contrast enhancement and entropy increase, witchmaking it easy to split and recognize, and human eye subjective judgment isalso much easier. It put forward a new research method about infrared targetrecognition
APA, Harvard, Vancouver, ISO, and other styles
9

Hasani, H., and F. Samadzadegan. "3D OBJECT CLASSIFICATION BASED ON THERMAL AND VISIBLE IMAGERY IN URBAN AREA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 11, 2015): 287–91. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-287-2015.

Full text
Abstract:
The spatial distribution of land cover in the urban area especially 3D objects (buildings and trees) is a fundamental dataset for urban planning, ecological research, disaster management, <i>etc</i>. According to recent advances in sensor technologies, several types of remotely sensed data are available from the same area. Data fusion has been widely investigated for integrating different source of data in classification of urban area. Thermal infrared imagery (TIR) contains information on emitted radiation and has unique radiometric properties. However, due to coarse spatial resolution of thermal data, its application has been restricted in urban areas. On the other hand, visible image (VIS) has high spatial resolution and information in visible spectrum. Consequently, there is a complementary relation between thermal and visible imagery in classification of urban area. This paper evaluates the potential of aerial thermal hyperspectral and visible imagery fusion in classification of urban area. In the pre-processing step, thermal imagery is resampled to the spatial resolution of visible image. Then feature level fusion is applied to construct hybrid feature space include visible bands, thermal hyperspectral bands, spatial and texture features and moreover Principle Component Analysis (PCA) transformation is applied to extract PCs. Due to high dimensionality of feature space, dimension reduction method is performed. Finally, Support Vector Machines (SVMs) classify the reduced hybrid feature space. The obtained results show using thermal imagery along with visible imagery, improved the classification accuracy up to 8% respect to visible image classification.
APA, Harvard, Vancouver, ISO, and other styles
10

Khodaei, B., F. Samadzadegan, F. Dadras Javan, and H. Hasani. "3D SURFACE GENERATION FROM AERIAL THERMAL IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 11, 2015): 401–5. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-401-2015.

Full text
Abstract:
Aerial thermal imagery has been recently applied to quantitative analysis of several scenes. For the mapping purpose based on aerial thermal imagery, high accuracy photogrammetric process is necessary. However, due to low geometric resolution and low contrast of thermal imaging sensors, there are some challenges in precise 3D measurement of objects. In this paper the potential of thermal video in 3D surface generation is evaluated. In the pre-processing step, thermal camera is geometrically calibrated using a calibration grid based on emissivity differences between the background and the targets. Then, Digital Surface Model (DSM) generation from thermal video imagery is performed in four steps. Initially, frames are extracted from video, then tie points are generated by Scale-Invariant Feature Transform (SIFT) algorithm. Bundle adjustment is then applied and the camera position and orientation parameters are determined. Finally, multi-resolution dense image matching algorithm is used to create 3D point cloud of the scene. Potential of the proposed method is evaluated based on thermal imaging cover an industrial area. The thermal camera has 640×480 Uncooled Focal Plane Array (UFPA) sensor, equipped with a 25 mm lens which mounted in the Unmanned Aerial Vehicle (UAV). The obtained results show the comparable accuracy of 3D model generated based on thermal images with respect to DSM generated from visible images, however thermal based DSM is somehow smoother with lower level of texture. Comparing the generated DSM with the 9 measured GCPs in the area shows the Root Mean Square Error (RMSE) value is smaller than 5 decimetres in both X and Y directions and 1.6 meters for the Z direction.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Thermal imagery"

1

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
3

Ward, Jason T. "Realistic texture in simulated thermal infrared imagery /." Online version of thesis, 2008. http://hdl.handle.net/1850/7067.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
5

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/.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
Abstract:
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 bildbehandlingen
In 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.
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
Abstract:
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

APA, Harvard, Vancouver, ISO, and other styles
9

Magnabosco, Marina. "Self localization and mapping using optical and thermal imagery." Thesis, Cranfield University, 2011. http://dspace.lib.cranfield.ac.uk/handle/1826/6704.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
10

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Thermal imagery"

1

Behrens, Richard J. Change detection analysis with spectral thermal imagery. Monterey, Calif: Naval Postgraduate School, 1998.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Cramer, K. Elliott. Thermal nondestructive characterization of corrosion in boiler tubes by application of a moving line heat source. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 2000.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Scrofani, James William. An adaptive method for the enhanced fusion of low-light visible and uncooled thermal infrared imagery. Monterey, Calif: Naval Postgraduate School, 1997.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

J, Le Vourch, ed. Atlas des fronts thermiques en mer Méditerranée d'après l'imagerie satellitaire =: Atlas of thermal fronts of the Mediterranean Sea derived from satellite imagery. Monaco: Institut océanographique, 1992.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

Koprowski, Robert. Processing Medical Thermal Images. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-61340-6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Amon, Francine K. Evaluation of image quality of thermal imagers used by the fire service. Gaithersburg, Md.]: U.S. Dept. of Commerce, National Institute of Standards and Technology, 2009.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

Kroll, Dorothy. Thermal and non-thermal food processing trends. Norwalk, CT: Business Communications Co., 1999.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

United States. National Aeronautics and Space Administration., ed. Planetary Hyperspectral Imager (PHI): PIDDP, final report. Danbury, CT: Hughes Danbury Optical Systems, 1996.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
9

Great Britain. Department of Health., ed. An Assessment of the Fuji film thermal imager FTI 200. London: Department of Health, NHS Procurement Directorate, Supplies Technology Division, 1990.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

Percy, Gilbert, and United States. National Aeronautics and Space Administration., eds. Computer control of a scanning electron microscope for digital image processing of thermal-wave images. [Washington, DC]: National Aeronautics and Space Administration, 1987.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Thermal imagery"

1

Castillo, José Carlos, Juan Serrano-Cuerda, Antonio Fernández-Caballero, and María T. López. "Segmenting Humans from Mobile Thermal Infrared Imagery." In Lecture Notes in Computer Science, 334–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02267-8_36.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Gade, Rikke, and Thomas B. Moeslund. "Classification of Sports Types Using Thermal Imagery." In Computer Vision in Sports, 209–27. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09396-3_10.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Vong, André, João P. Matos-Carvalho, Dário Pedro, Slavisa Tomic, Marko Beko, Fábio Azevedo, Sérgio D. Correia, and André Mora. "Open-Source Mapping Method Applied to Thermal Imagery." In Lecture Notes in Networks and Systems, 43–57. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10461-9_3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Gupta, Umesh, and Preetisudha Meher. "Statistical Analysis of Target Tracking Algorithms in Thermal Imagery." In Cognitive Informatics and Soft Computing, 635–46. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1451-7_65.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Xiao, Yonghao, Hong Zheng, and Weiyu Yu. "Automatic Crowd Detection Based on Unmanned Aerial Vehicle Thermal Imagery." In Advances in Intelligent Systems and Computing, 510–16. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65978-7_77.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Sharma, Kul Vaibhav, Sumit Khandelwal, and Nivedita Kaul. "Intensity Transformation Fusion of Landsat 8 Thermal Infrared (TIR) Imagery." In Advances in Intelligent Systems and Computing, 214–20. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39875-0_23.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Kieu, My, Andrew D. Bagdanov, Marco Bertini, and Alberto del Bimbo. "Task-Conditioned Domain Adaptation for Pedestrian Detection in Thermal Imagery." In Computer Vision – ECCV 2020, 546–62. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58542-6_33.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Kieu, My, Andrew D. Bagdanov, Marco Bertini, and Alberto Del Bimbo. "Domain Adaptation for Privacy-Preserving Pedestrian Detection in Thermal Imagery." In Lecture Notes in Computer Science, 203–13. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30645-8_19.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Dransfeld, S. "Current Tracking in the Mediterranean Sea Using Thermal Satellite Imagery." In Remote Sensing of the European Seas, 165–76. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6772-3_13.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Sidiropoulou-Velidou, Dafni, Andreas Georgopoulos, and José Luis Lerma. "Exploitation of Thermal Imagery for the Detection of Pathologies in Monuments." In Progress in Cultural Heritage Preservation, 97–108. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34234-9_10.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Thermal imagery"

1

Collins, Brian H., Richard C. Olsen, and John A. Hackwell. "Thermal imagery spectral analysis." In Optical Science, Engineering and Instrumentation '97, edited by Michael R. Descour and Sylvia S. Shen. SPIE, 1997. http://dx.doi.org/10.1117/12.278929.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Saponaro, Philip, Scott Sorensen, Abhishek Kolagunda, and Chandra Kambhamettu. "Material classification with thermal imagery." In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2015. http://dx.doi.org/10.1109/cvpr.2015.7299096.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Althouse, Mark L., Chein-I. Chang, and David C. Smith. "Single-frame multispectral thermal imagery." In Aerospace Sensing, edited by Gerald C. Holst. SPIE, 1992. http://dx.doi.org/10.1117/12.137972.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Martinez, Brais, Xavier Binefa, and Maja Pantic. "Facial component detection in thermal imagery." In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops). IEEE, 2010. http://dx.doi.org/10.1109/cvprw.2010.5543605.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Kuang, Hai-lan, William Perrie, Wei Chen, Tao Xie, Xin-hua Liu, and Biao Zhang. "Thermal front retreivals from SAR imagery." In IGARSS 2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2012. http://dx.doi.org/10.1109/igarss.2012.6350387.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Kieu, My, Lorenzo Berlincioni, Leonardo Galteri, Marco Bertini, Andrew D. Bagdanov, and Alberto del Bimbo. "Robust pedestrian detection in thermal imagery using synthesized images." In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9412764.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Ustun, Berkcan, and Ezgi Cakir Ayerden. "Active Domain Adaptation with Generated Images for Thermal Imagery." In 2023 31st Signal Processing and Communications Applications Conference (SIU). IEEE, 2023. http://dx.doi.org/10.1109/siu59756.2023.10223748.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Yu, Jay, Zohaib Khan, Elysia Guglielmo, and Bin Lee. "A comparison of synthetic thermal imagery created using MuSES and thermal imagery captured in the field." In Target and Background Signatures VIII, edited by Karin Stein and Ric Schleijpen. SPIE, 2022. http://dx.doi.org/10.1117/12.2635147.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Gordon, Christopher, Mark Acosta, Nathan Short, Shuowen Hu, and Alex L. Chan. "Toward automated face detection in thermal and polarimetric thermal imagery." In SPIE Defense + Security, edited by Ivan Kadar. SPIE, 2016. http://dx.doi.org/10.1117/12.2222578.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Deilamsalehy, Hanieh, Timothy C. Havens, and Pasi Lautala. "Sensor Fusion of Wayside Visible and Thermal Imagery for Rail Car Wheel and Bearing Damage Detection." In 2017 Joint Rail Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/jrc2017-2284.

Full text
Abstract:
Two major components of rolling stock that are always of great interest when it comes to maintenance and safety related issues are car wheels and bearings. Rail car wheels are subjected to a variety of damage types due to their interaction with the track and brakes. It is important for the rail industry to detect these defects and take proper action at an early stage, before more damage can be caused to the train or possibly the track and to prevent possible safety hazards. Different inspection sensors and systems, such as wheel impact monitors, wheel profile detectors, hotbox detectors and acoustic detection technologies, are employed to detect different types of wheel and bearing defects. Usually no single sensor can accurately detect all kinds of damages and hence a combination of different sensors and systems and manual inspection by experts is used for wheel maintenance purposes and to guarantee train safety. The more complete and accurate the automatic defect detections are, the less manual examination is necessary, leading to potential savings in inspection time/resources and rail car maintenance costs. Wayside thermal and visible spectrum cameras are one option for the automatic wheel and bearing inspection. Each of these sensors has their own strengths and weaknesses. There are some types of defects that are not detectable at an early stage in the images taken by a vision camera, however these defects generate a distinctive heat pattern on the wheel or bearing that is clearly visible in the thermal imagery. On the other hand, other damages might be detectable from the visible spectrum image, but not necessarily have a distinguishable heat pattern in the thermal imagery. Since a thermal image is basically built of solely temperature data, it excludes other critical information, such as texture or color. This makes thermal and visible spectrum imagery complementary and if the images are fused the result will benefit from the strengths of both sensors. In this paper, wavelet decomposition is employed to extract the features of the thermal and vision imagery. Then the two images are merged based on their decompositions and a fused image is composed. The resulting fused image contains more information than each individual image and can be used as an input for image-based wheel and bearing defect detection algorithms. To verify the proposed method and to show an example of this application, it is demonstrated on a real data set from a Union Pacific rail line to identify sliding wheels.
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Thermal imagery"

1

Cohen, Yafit, Carl Rosen, Victor Alchanatis, David Mulla, Bruria Heuer, and Zion Dar. Fusion of Hyper-Spectral and Thermal Images for Evaluating Nitrogen and Water Status in Potato Fields for Variable Rate Application. United States Department of Agriculture, November 2013. http://dx.doi.org/10.32747/2013.7594385.bard.

Full text
Abstract:
Potato yield and quality are highly dependent on an adequate supply of nitrogen and water. Opportunities exist to use airborne hyperspectral (HS) remote sensing for the detection of spatial variation in N status of the crop to allow more targeted N applications. Thermal remote sensing has the potential to identify spatial variations in crop water status to allow better irrigation management and eventually precision irrigation. The overall objective of this study was to examine the ability of HS imagery in the visible and near infrared spectrum (VIS-NIR) and thermal imagery to distinguish between water and N status in potato fields. To lay the basis for achieving the research objectives, experiments in the US and in Israel were conducted in potato with different irrigation and N-application amounts. Thermal indices based merely on thermal images were found sensitive to water status in both Israel and the US in three potato varieties. Spectral indices based on HS images were found suitable to detect N stress accurately and reliably while partial least squares (PLS) analysis of spectral data was more sensitive to N levels. Initial fusion of HS and thermal images showed the potential of detecting both N stress and water stress and even to differentiate between them. This study is one of the first attempts at fusing HS and thermal imagery to detect N and water stress and to estimate N and water levels. Future research is needed to refine these techniques for use in precision agriculture applications.
APA, Harvard, Vancouver, ISO, and other styles
2

Socolinsky, Diego A., Lawrence B. Wolff, Joshua D. Neuheisel, and Christopher K. Eveland. Illumination Invariant Face Recognition Using Thermal Infrared Imagery. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada444367.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Walsh, Stephen J., Mark F. Tardiff, Lawrence K. Chilton, and Candace N. Metoyer. Effect of Background Emissivity on Gas Detection in Thermal Hyperspectral Imagery. Office of Scientific and Technical Information (OSTI), October 2008. http://dx.doi.org/10.2172/943410.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Selinger, Andrea, and Diego A. Socolinsky. Appearance-Based Facial Recognition Using Visible and Thermal Imagery: A Comparative Study. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada444419.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Socolinsky, Diego A., and Andrea Selinger. A Comparative Analysis of Face Recognition Performance With Visible and Thermal Infrared Imagery. Fort Belvoir, VA: Defense Technical Information Center, January 2002. http://dx.doi.org/10.21236/ada453159.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Adam Bernstein. Monitoring large enrichment plants using thermal imagery from commercial satellites: A case study. Office of Scientific and Technical Information (OSTI), May 2000. http://dx.doi.org/10.2172/756340.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Green, Mary K. Multispectral Thermal Imagery and Its Application to the Geologic Mapping of the Koobi Fora Formation, Northwestern Kenya. Office of Scientific and Technical Information (OSTI), December 2005. http://dx.doi.org/10.2172/882918.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Rencz, A. N., C. Bowie, and B. C. Ward. Application of thermal imagery from LANDSAT data to locate kimberlites, Lac de Gras area, district of Mackenzie, N.W.T. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1996. http://dx.doi.org/10.4095/211837.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Christie, Benjamin, Osama Ennasr, and Garry Glaspell. ROS integrated object detection for SLAM in unknown, low-visibility environments. Engineer Research and Development Center (U.S.), November 2021. http://dx.doi.org/10.21079/11681/42385.

Full text
Abstract:
Integrating thermal (or infrared) imagery on a robotics platform allows Unmanned Ground Vehicles (UGV) to function in low-visibility environments, such as pure darkness or low-density smoke. To maximize the effectiveness of this approach we discuss the modifications required to integrate our low-visibility object detection model on a Robot Operating System (ROS). Furthermore, we introduce a method for reporting detected objects while performing Simultaneous Localization and Mapping (SLAM) by generating bounding boxes and their respective transforms in visually challenging environments.
APA, Harvard, Vancouver, ISO, and other styles
10

Clausen, Jay, Christopher Felt, Michael Musty, Vuong Truong, Susan Frankenstein, Anna Wagner, Rosa Affleck, Steven Peckham, and Christopher Williams. Modernizing environmental signature physics for target detection—Phase 3. Engineer Research and Development Center (U.S.), March 2022. http://dx.doi.org/10.21079/11681/43442.

Full text
Abstract:
The present effort (Phase 3) builds on our previously published prior efforts (Phases 1 and 2), which examined methods of determining the probability of detection and false alarm rates using thermal infrared for buried object detection. Environmental phenomenological effects are often represented in weather forecasts in a relatively coarse, hourly resolution, which introduces concerns such as exclusion or misrepresentation of ephemera or lags in timing when using this data as an input for the Army’s Tactical Assault Kit software system. Additionally, the direct application of observed temperature data with weather model data may not be the best approach because metadata associated with the observations are not included. As a result, there is a need to explore mathematical methods such as Bayesian statistics to incorporate observations into models. To better address this concern, the initial analysis in Phase 2 data is expanded in this report to include (1) multivariate analyses for detecting objects in soil, (2) a moving box analysis of object visibility with alternative methods for converting FLIR radiance values to thermal temperature values, (3) a calibrated thermal model of soil temperature using thermal IR imagery, and (4) a simple classifier method for automating buried object detection.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Thermal imagery' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography