Relevant bibliographies by topics / HYPER/MULTISPECTRAL IMAGERY

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

Academic literature on the topic 'HYPER/MULTISPECTRAL IMAGERY'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "HYPER/MULTISPECTRAL IMAGERY"

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Undrajavarapu, Jemima, and M. Chandra Sekhar. "Hyper Spectral Remote Sensing for Mapping Species and Characteristics of Mangroves in Krishna Delta Region." Current World Environment 15, no. 3 (December 30, 2020): 613–18. http://dx.doi.org/10.12944/cwe.15.3.25.

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Mangroves are globally classified as eastern and western groups of which 40% are found along Asian coasts. The largest identified mangroves are found in Indonesia, Brazil, Sunder bans of India and Bangladesh. Odum 1971 in his research referred mangroves serve as Juvenile stock and form most valuable Biomass. In the state of Andhra Pradesh the mangroves are concentrated in the deltas of Krishna and Godavari which add a healthy ecosystem. An extensive research in monitoring the nature and changes of Godavari delta mangroves using Remote sensing technologies. The mangroves are vastly reserves of different species of flora and are classified as single vegetative class in traditional multispectral imagery, where there is a possibility of losing information due to specific narrow band widths. Hence an attempt is being made for species level classification and characterization of Krishna delta region using hyper spectral remote sensing imagery. Hyper spectral remote sensing overcomes the limitation of extensive field work which is labor intensive and costly. The current research paper describes the species level classification of flora in Krishna delta using Hyper spectral remote sensing imagery.
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Hu, Ting, Hongyan Zhang, Huanfeng Shen, and Liangpei Zhang. "Robust Registration by Rank Minimization for Multiangle Hyper/Multispectral Remotely Sensed Imagery." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, no. 6 (June 2014): 2443–57. http://dx.doi.org/10.1109/jstars.2014.2311585.

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Zhou, Jing, Biwen Wang, Jiahao Fan, Yuchi Ma, Yi Wang, and Zhou Zhang. "A Systematic Study of Estimating Potato N Concentrations Using UAV-Based Hyper- and Multi-Spectral Imagery." Agronomy 12, no. 10 (October 17, 2022): 2533. http://dx.doi.org/10.3390/agronomy12102533.

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Potato growth depends largely on nitrogen (N) availability in the soil. However, the shallow-root crop coupled with its common cultivation in coarse-textured soils leads to its poor N use efficiency. Fast and accurate estimations of potato tissue N concentrations are urgently needed to assist the decision making in precision fertilization management. Remote sensing has been utilized to evaluate the potato N status by correlating spectral information with lab tests on leaf N concentrations. In this study, a systematic comparison was conducted to quantitatively evaluate the performance of hyperspectral and multispectral images in estimating the potato N status, providing a reference for the trade-off between sensor costs and performance. In the experiment, two potato varieties were planted under four fertilization rates with replicates. UAV images were acquired multiple times during the season with a narrow-band hyperspectral imager. Multispectral reflectance was simulated by merging the relevant narrow bands into broad bands to mimic commonly used multispectral cameras. The whole leaf total N concentration and petiole nitrate-N concentration were obtained from 160 potato leaf samples. A partial least square regression model was developed to estimate the two N status indicators using different groups of image features. The best estimation accuracies were given by reflectance of the full spectra with 2.2 nm narrow, with the coefficient of determination (R2) being 0.78 and root mean square error (RMSE) being 0.41 for the whole leaf total N concentration; while, for the petiole nitrate-N concentration, the 10 nm bands had the best performance (R2 = 0.87 and RMSE = 0.13). Generally, the model performance decreased with an increase of the spectral bandwidth. The hyperspectral full spectra largely outperformed all three multispectral cameras, but there was no significant difference among the three brands of multispectral cameras. The results also showed that spectral bands in the visible regions (400–700 nm) were the most highly correlated with potato N concentrations.
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Chen Shan-Jing, Hu Yi-Hua, Sun Du-Juan, and Xu Shi-Long. "A simulation method by air and space integrated fusion based on hyper-/multispectral imagery." Acta Physica Sinica 62, no. 20 (2013): 204201. http://dx.doi.org/10.7498/aps.62.204201.

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Sharif, I., and S. Khare. "Comparative Analysis of Haar and Daubechies Wavelet for Hyper Spectral Image Classification." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 937–41. http://dx.doi.org/10.5194/isprsarchives-xl-8-937-2014.

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With the number of channels in the hundreds instead of in the tens Hyper spectral imagery possesses much richer spectral information than multispectral imagery. The increased dimensionality of such Hyper spectral data provides a challenge to the current technique for analyzing data. Conventional classification methods may not be useful without dimension reduction pre-processing. So dimension reduction has become a significant part of Hyper spectral image processing. This paper presents a comparative analysis of the efficacy of Haar and Daubechies wavelets for dimensionality reduction in achieving image classification. Spectral data reduction using Wavelet Decomposition could be useful because it preserves the distinction among spectral signatures. Daubechies wavelets optimally capture the polynomial trends while Haar wavelet is discontinuous and resembles a step function. The performance of these wavelets are compared in terms of classification accuracy and time complexity. This paper shows that wavelet reduction has more separate classes and yields better or comparable classification accuracy. In the context of the dimensionality reduction algorithm, it is found that the performance of classification of Daubechies wavelets is better as compared to Haar wavelet while Daubechies takes more time compare to Haar wavelet. The experimental results demonstrate the classification system consistently provides over 84% classification accuracy.
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Olivetti, Diogo, Rejane Cicerelli, Jean-Michel Martinez, Tati Almeida, Raphael Casari, Henrique Borges, and Henrique Roig. "Comparing Unmanned Aerial Multispectral and Hyperspectral Imagery for Harmful Algal Bloom Monitoring in Artificial Ponds Used for Fish Farming." Drones 7, no. 7 (June 21, 2023): 410. http://dx.doi.org/10.3390/drones7070410.

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This work aimed to assess the potential of unmanned aerial vehicle (UAV) multi- and hyper-spectral platforms to estimate chlorophyll-a (Chl-a) and cyanobacteria in experimental fishponds in Brazil. In addition to spectral resolutions, the tested platforms differ in the price, payload, imaging system, and processing. Hyperspectral airborne surveys were conducted using a push-broom system 276-band Headwall Nano-Hyperspec camera onboard a DJI Matrice 600 UAV. Multispectral airborne surveys were conducted using a global shutter-frame 4-band Parrot Sequoia camera onboard a DJI Phantom 4 UAV. Water quality field measurements were acquired using a portable fluorometer and laboratory analysis. The concentration ranged from 14.3 to 290.7 µg/L and from 0 to 112.5 µg/L for Chl-a and cyanobacteria, respectively. Forty-one Chl-a and cyanobacteria bio-optical retrieval models were tested. The UAV hyperspectral image achieved robust Chl-a and cyanobacteria assessments, with RMSE values of 32.8 and 12.1 µg/L, respectively. Multispectral images achieved Chl-a and cyanobacteria retrieval with RMSE values of 47.6 and 35.1 µg/L, respectively, efficiently mapping the broad Chl-a concentration classes. Hyperspectral platforms are ideal for the robust monitoring of Chl-a and CyanoHABs; however, the integrated platform has a high cost. More accessible multispectral platforms may represent a trade-off between the mapping efficiency and the deployment costs, provided that the multispectral cameras offer narrow spectral bands in the 660–690 nm and 700–730 nm ranges for Chl-a and in the 600–625 nm and 700–730 nm spectral ranges for cyanobacteria.
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Honkavaara, E., R. Näsi, R. Oliveira, N. Viljanen, J. Suomalainen, E. Khoramshahi, T. Hakala, et al. "USING MULTITEMPORAL HYPER- AND MULTISPECTRAL UAV IMAGING FOR DETECTING BARK BEETLE INFESTATION ON NORWAY SPRUCE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 429–34. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-429-2020.

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Abstract. Various biotic and abiotic stresses are threatening forests. Modern remote sensing technologies provide powerful means for monitoring forest health, and provide a sustainable basis for forest management and protection. The objective of this study was to develop unmanned aerial vehicle (UAV) based spectral remote sensing technologies for tree health assessment, particularly, for detecting the European spruce bark beetle (Ips typographus L.) attacks. Our focus was to study the early detection of bark beetle attack, i.e. the “green attack” phase. This is a difficult remote sensing task as there does not exist distinct symptoms that can be observed by the human eye. A test site in a Norway spruce (Picea abies (L.) Karst.) dominated forest was established in Southern-Finland in summer 2019. It had an emergent bark beetle outbreak and it was also suffering from other stress factors, especially the root and butt rot (Heterobasidion annosum (Fr.) Bref. s. lato). Altogether seven multitemporal hyper- and multispectral UAV remote sensing datasets were captured from the area in August to October 2019. Firstly, we explored deterioration of tree health and development of spectral symptoms using a time series of UAV hyperspectral imagery. Secondly, we trained assessed a machine learning model for classification of spruce health into classes of “bark beetle green attack”, “root-rot”, and “healthy”. Finally, we demonstrated the use of the model in tree health mapping in a test area. Our preliminary results were promising and indicated that the green attack phase could be detected using the accurately calibrated spectral image data.
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DWIJESH H P, JAYANTH, SANDEEP S. V, and RASHMI S. "Computerized or Automated Object Recognition and Analysis of Pattern Matching in Runways Using Surface Track Data." Journal of University of Shanghai for Science and Technology 23, no. 11 (November 6, 2021): 159–65. http://dx.doi.org/10.51201/jusst/21/10867.

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In today’s world, accurate and fast information is vital for safe aircraft landings. The purpose of an EMAS (Engineered Materials Arresting System) is to prevent an aeroplane from overrunning with no human injury and minimal damage to the aircraft. Although various algorithms for object detection analysis have been developed, only a few researchers have examined image analysis as a landing assist. Image intensity edges are employed in one system to detect the sides of a runway in an image sequence, allowing the runway’s 3-dimensional position and orientation to be approximated. A fuzzy network system is used to improve object detection and extraction from aerial images. In another system, multi-scale, multiplatform imagery is used to combine physiologically and geometrically inspired algorithms for recognizing objects from hyper spectral and/or multispectral (HS/MS) imagery. However, the similarity in the top view of runways, buildings, highways, and other objects is a disadvantage of these methods. We propose a new method for detecting and tracking the runway based on pattern matching and texture analysis of digital images captured by aircraft cameras. Edge detection techniques are used to recognize runways from aerial images. The edge detection algorithms employed in this paper are the Hough Transform, Canny Filter, and Sobel Filter algorithms, which result in efficient detection.
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Pham, Tien Dat, Junshi Xia, Nam Thang Ha, Dieu Tien Bui, Nga Nhu Le, and Wataru Tekeuchi. "A Review of Remote Sensing Approaches for Monitoring Blue Carbon Ecosystems: Mangroves, Seagrassesand Salt Marshes during 2010–2018." Sensors 19, no. 8 (April 24, 2019): 1933. http://dx.doi.org/10.3390/s19081933.

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Blue carbon (BC) ecosystems are an important coastal resource, as they provide a range of goods and services to the environment. They play a vital role in the global carbon cycle by reducing greenhouse gas emissions and mitigating the impacts of climate change. However, there has been a large reduction in the global BC ecosystems due to their conversion to agriculture and aquaculture, overexploitation, and removal for human settlements. Effectively monitoring BC ecosystems at large scales remains a challenge owing to practical difficulties in monitoring and the time-consuming field measurement approaches used. As a result, sensible policies and actions for the sustainability and conservation of BC ecosystems can be hard to implement. In this context, remote sensing provides a useful tool for mapping and monitoring BC ecosystems faster and at larger scales. Numerous studies have been carried out on various sensors based on optical imagery, synthetic aperture radar (SAR), light detection and ranging (LiDAR), aerial photographs (APs), and multispectral data. Remote sensing-based approaches have been proven effective for mapping and monitoring BC ecosystems by a large number of studies. However, to the best of our knowledge, this is the first comprehensive review on the applications of remote sensing techniques for mapping and monitoring BC ecosystems. The main goal of this review is to provide an overview and summary of the key studies undertaken from 2010 onwards on remote sensing applications for mapping and monitoring BC ecosystems. Our review showed that optical imagery, such as multispectral and hyper-spectral data, is the most common for mapping BC ecosystems, while the Landsat time-series are the most widely-used data for monitoring their changes on larger scales. We investigate the limitations of current studies and suggest several key aspects for future applications of remote sensing combined with state-of-the-art machine learning techniques for mapping coastal vegetation and monitoring their extents and changes.
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Pereira-Sandoval, Marcela, Ana Ruescas, Patricia Urrego, Antonio Ruiz-Verdú, Jesús Delegido, Carolina Tenjo, Xavier Soria-Perpinyà, Eduardo Vicente, Juan Soria, and José Moreno. "Evaluation of Atmospheric Correction Algorithms over Spanish Inland Waters for Sentinel-2 Multi Spectral Imagery Data." Remote Sensing 11, no. 12 (June 21, 2019): 1469. http://dx.doi.org/10.3390/rs11121469.

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The atmospheric contribution constitutes about 90 percent of the signal measured by satellite sensors over oceanic and inland waters. Over open ocean waters, the atmospheric contribution is relatively easy to correct as it can be assumed that water-leaving radiance in the near-infrared (NIR) is equal to zero and it can be performed by applying a relatively simple dark-pixel-correction-based type of algorithm. Over inland and coastal waters, this assumption cannot be made since the water-leaving radiance in the NIR is greater than zero due to the presence of water components like sediments and dissolved organic particles. The aim of this study is to determine the most appropriate atmospheric correction processor to be applied on Sentinel-2 MultiSpectral Imagery over several types of inland waters. Retrievals obtained from different atmospheric correction processors (i.e., Atmospheric correction for OLI ‘lite’ (ACOLITE), Case 2 Regional Coast Colour (here called C2RCC), Case 2 Regional Coast Colour for Complex waters (here called C2RCCCX), Image correction for atmospheric effects (iCOR), Polynomial-based algorithm applied to MERIS (Polymer) and Sen2Cor or Sentinel 2 Correction) are compared against in situ reflectance measured in lakes and reservoirs in the Valencia region (Spain). Polymer and C2RCC are the processors that give back the best statistics, with coefficients of determination higher than 0.83 and mean average errors less than 0.01. An evaluation of the performance based on water types and single bands–classification based on ranges of in situ chlorophyll-a concentration and Secchi disk depth values- showed that performance of these set of processors is better for relatively complex waters. ACOLITE, iCOR and Sen2Cor had a better performance when applied to meso- and hyper-eutrophic waters, compare with oligotrophic. However, other considerations should also be taken into account, like the elevation of the lakes above sea level, their distance from the sea and their morphology.
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Dissertations / Theses on the topic "HYPER/MULTISPECTRAL IMAGERY"

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Carmody, James Daniel Physical Environmental &amp Mathematical Sciences Australian Defence Force Academy UNSW. "Deriving bathymetry from multispectral and hyperspectral imagery." Awarded by:University of New South Wales - Australian Defence Force Academy. School of Physical, Environmental and Mathematical Sciences, 2007. http://handle.unsw.edu.au/1959.4/38654.

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Knowledge of water depth is a crucial for planning military amphibious operations. Bathymetry from remote sensing with multispectral or hyperspectral imagery provides an opportunity to acquire water depth data faster than traditional hydrographic survey methods without the need to deploy a hydrographic survey vessel. It also provides a means of collecting bathymetric data covertly. This research explores two techniques for deriving bathymetry and assesses them for use by those involved in providing support to military operations. To support this aim a fieldwork campaign was undertaken in May, 2000, in northern Queensland. The fieldwork collected various inherent and apparent water optical properties and was concurrent with airborne hyperspectral imagery collection, space-based multispectral imagery collection and a hydrographic survey. The water optical properties were used to characterise the water and to understand how they affect deriving bathymetry from imagery. The hydrographic data was used to assess the performance of the bathymetric techniques. Two methods for deriving bathymetry were trialled. One uses a ratio of subsurface irradiance reflectance at two wavelengths and then tunes the result with known water depths. The other inverts the radiative transfer equation utilising the optical properties of the water to derive water depth. Both techniques derived water depth down to approximately six to seven metres. At that point the Cowley Beach waters became optically deep. Sensitivity analysis of the inversion method found that it was most sensitive to errors in vertical attenuation Kd and to errors in transforming the imagery into subsurface irradiance reflectance, R(0-) units. Both techniques require a priori knowledge to derive depth and a more sophisticated approach would be required to determine water depth without prior knowledge of the area of interest. This research demonstrates that water depth can be accurately mapped with optical techniques in less than ideal optical conditions. It also demonstrates that the collection of inherent and apparent optical properties is important for validating remotely sensed imagery.
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Martínez, Usó Adolfo. "Unsupervised Band Selection and Segmentation in Hyper/Multispectral Images." Doctoral thesis, Universitat Jaume I, 2008. http://hdl.handle.net/10803/10483.

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The title of the thesis focuses the attention on hyperspectral image segmentation, that is, we want to detect salient regions in a hyperspectral image and isolate them as accurate as possible. This purpose presents two main problems: Firstly, the fact of using hyperspectral imaging not only give us a huge amount of information, but we also have to face the problem of selecting somehow the information avoiding redundancies.
Secondly, the problem of segmentation strictly speaking is still a challenging question whatever the input image would be.
This thesis is focused on solving the whole process by means of building an image processing method that analyses and optimises the information acquired by a multispectral device. After that, it detects the main regions that are present in the scene in an image segmentation procedure. Therefore, this work will be divided into two parts. In the first part, an approach for selecting the most relevant subset of input bands will be presented. In the second part, this reduced representation of the initial bands will be the input data of a segmentation method.
Finally, the main contributions of this PhD work could be briefly summarised as follows. On the one hand, we have proposed a pre-processing stage with an unsupervised band selection approach based on information measures that reduces considerably the amount of data. This approach has been successfully compared with well-known algorithms of the literature, showing its good performance with regard to pixel image classification tasks. On the other hand, after the band selection stage, two unsupervised segmentation procedures for detecting the main parts in multispectral images have been also developed. Regarding to this segmentation part, we have mainly contributed with two measures of similarity among regions. An objective functional for selecting an optimal (or close to optimal) partition of the image is another relevant contribution too.
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Benhalouche, Fatima Zohra. "Méthodes de démélange et de fusion des images multispectrales et hyperspectrales de télédétection spatiale." Thesis, Toulouse 3, 2018. http://www.theses.fr/2018TOU30083/document.

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Au cours de cette thèse, nous nous sommes intéressés à deux principales problématiques de la télédétection spatiale de milieux urbains qui sont : le "démélange spectral " et la "fusion". Dans la première partie de la thèse, nous avons étudié le démélange spectral d'images hyperspectrales de scènes de milieux urbains. Les méthodes développées ont pour objectif d'extraire, d'une manière non-supervisée, les spectres des matériaux présents dans la scène imagée. Le plus souvent, les méthodes de démélange spectral (méthodes dites de séparation aveugle de sources) sont basées sur le modèle de mélange linéaire. Cependant, lorsque nous sommes en présence de paysage non-plat, comme c'est le cas en milieu urbain, le modèle de mélange linéaire n'est plus valide et doit être remplacé par un modèle de mélange non-linéaire. Ce modèle non-linéaire peut être réduit à un modèle de mélange linéaire-quadratique/bilinéaire. Les méthodes de démélange spectral proposées sont basées sur la factorisation matricielle avec contrainte de non-négativité, et elles sont conçues pour le cas particulier de scènes urbaines. Les méthodes proposées donnent généralement de meilleures performances que les méthodes testées de la littérature. La seconde partie de cette thèse à été consacrée à la mise en place de méthodes qui permettent la fusion des images multispectrale et hyperspectrale, afin d'améliorer la résolution spatiale de l'image hyperspectrale. Cette fusion consiste à combiner la résolution spatiale élevée des images multispectrales et la haute résolution spectrale des images hyperspectrales. Les méthodes mises en place sont des méthodes conçues pour le cas particulier de fusion de données de télédétection de milieux urbains. Ces méthodes sont basées sur des techniques de démélange spectral linéaire-quadratique et utilisent la factorisation en matrices non-négatives. Les résultats obtenus montrent que les méthodes développées donnent globalement des performances satisfaisantes pour la fusion des données hyperspectrale et multispectrale. Ils prouvent également que ces méthodes surpassent significativement les approches testées de la littérature
In this thesis, we focused on two main problems of the spatial remote sensing of urban environments which are: "spectral unmixing" and "fusion". In the first part of the thesis, we are interested in the spectral unmixing of hyperspectral images of urban scenes. The developed methods are designed to unsupervisely extract the spectra of materials contained in an imaged scene. Most often, spectral unmixing methods (methods known as blind source separation) are based on the linear mixing model. However, when facing non-flat landscape, as in the case of urban areas, the linear mixing model is not valid any more, and must be replaced by a nonlinear mixing model. This nonlinear model can be reduced to a linear-quadratic/bilinear mixing model. The proposed spectral unmixing methods are based on matrix factorization with non-negativity constraint, and are designed for urban scenes. The proposed methods generally give better performance than the tested literature methods. The second part of this thesis is devoted to the implementation of methods that allow the fusion of multispectral and hyperspectral images, in order to improve the spatial resolution of the hyperspectral image. This fusion consists in combining the high spatial resolution of multispectral images and high spectral resolution of hyperspectral images. The implemented methods are designed for urban remote sensing data. These methods are based on linear-quadratic spectral unmixing techniques and use the non-negative matrix factorization. The obtained results show that the developed methods give good performance for hyperspectral and multispectral data fusion. They also show that these methods significantly outperform the tested literature approaches
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PATEL, RISHI. "MATERIAL CLASS MAPPING BY REFLECTANCE MATCHING OF HYPER/MULTISPECTRAL IMAGERY." Thesis, 2017. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16037.

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The main objective of this study is material mapping in hyperspectral image with the help of spectral reflectance matching. Presence of reflectance curve is the heart and soul of any operation performed, considering hyperspectral imagery. Certain material shows unique or characteristic reflectance plot known as signature plot or footprint within the range of spectrum. This feature of Hyperspectral image is exploited in case of material mapping. Visible materials like road, plantation, rooftops etc or even invisible materials like, soil constituents (carbonates, Na, K salts, water presence) or even the presence of ores of minerals (e.g. cuprite, alunite) beneath the surface of the earth can be predicted with sufficient accuracy. Hyperspectral data pose challenges to image interpretation, because of the need for calibration, redundancy in information, and high data volume due to large dimensionality of the feature space. In this project, hyperspectral image classification, band reduction and new technique for hyperspectral image classification is proposed and implemented. Both visual and quantitative results are calculated with the help of matlab. This project also designs a basic toolbox in MATLAB for processing and classification of hyperspectral image. Processing of hyperspectral image is divided into five modules, each performing specific operation. First, Acquisition of hypermultispectral image and display of its basic properties. Second, Formulation of classes with the help of user selecting points over displayed hyper-multispectral image. For the images in which signature reflectance library is already available user help for the selection of spectral signature is not required. Third, Reduction of Dimension of hyperspectral image up to user specified number of bands and calculation of the amount of information lost. Fourth, Material class mapping by reflectance Matching of hyper\multispectral image using traditional SAS, SDS, SCS deterministic approach. New method proposed for material classification over traditional SAS, SDS, SCS approach, (regression transform is used over reflectance curve to obtained separate regression distance class matrix) displaying the result using windowing technique and enhancing the output using Floyd dithering technique. Fifth, this part calculates the number of pixels, amount of area classified under each class, processing time, accuracy comparison between traditional and proposed techniques. Proposed method shows considerable improvement both visually and quantitatively over previous method. For the data set downloaded who’s ground truth is not available, pictorial result is shown and quantitative analysis is done for the images with ground truth present.
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Books on the topic "HYPER/MULTISPECTRAL IMAGERY"

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Jia, Xiuping. Field Guide to Hyper/Multispectral Image Processing. SPIE, 2022.

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Book chapters on the topic "HYPER/MULTISPECTRAL IMAGERY"

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Kozma-Bognár, Veronika, and József Berke. "Determination of Optimal Hyper- and Multispectral Image Channels by Spectral Fractal Structure." In Lecture Notes in Electrical Engineering, 255–62. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06773-5_34.

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Conference papers on the topic "HYPER/MULTISPECTRAL IMAGERY"

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Dian, Yuanyong, Zengyuan Li, and Yong Pang. "Forest tree species clssification based on airborne hyper-spectral imagery." In Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Jinwen Tian and Jie Ma. SPIE, 2013. http://dx.doi.org/10.1117/12.2030554.

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Bernstein, L. S., S. M. Adler-Golden, R. L. Sundberg, and A. J. Ratkowski. "Improved reflectance retrieval from hyper- and multispectral imagery without prior scene or sensor information." In Remote Sensing, edited by James R. Slusser, Klaus Schäfer, and Adolfo Comerón. SPIE, 2006. http://dx.doi.org/10.1117/12.705038.

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Perkins, Timothy, Steven Adler-Golden, Michael Matthew, Alexander Berk, Gail Anderson, James Gardner, and Gerald Felde. "Retrieval of atmospheric properties from hyper and multispectral imagery with the FLAASH atmospheric correction algorithm." In Remote Sensing, edited by Klaus Schäfer, Adolfo Comerón, James R. Slusser, Richard H. Picard, Michel R. Carleer, and Nicolaos I. Sifakis. SPIE, 2005. http://dx.doi.org/10.1117/12.626526.

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Conant, John A., and Kurt D. Annen. "Automated hyper/multispectral image analysis tool." In Aerospace/Defense Sensing, Simulation, and Controls, edited by Sylvia S. Shen and Michael R. Descour. SPIE, 2001. http://dx.doi.org/10.1117/12.437003.

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Mehta, Sanjeev, Kuhelika Bera, and R. M. Parmar. "Camera electronics for hyper-spectral imager." In Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications II. SPIE, 2008. http://dx.doi.org/10.1117/12.806225.

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Aiazzi, Bruno, Luciano Alparone, Alberto Arienzo, Andrea Garzelli, and Simone Lolli. "Fast multispectral pansharpening based on a hyper-ellipsoidal color space." In Image and Signal Processing for Remote Sensing XXV, edited by Lorenzo Bruzzone, Francesca Bovolo, and Jon Atli Benediktsson. SPIE, 2019. http://dx.doi.org/10.1117/12.2533481.

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Guérineau, Nicolas, Guillaume Druart, Frédéric Gillard, Yann Ferrec, Mathieu Chambon, Sylvain Rommeluère, Grégory Vincent, Riad Haïdar, Jean Taboury, and Manuel Fendler. "Compact designs of hyper- or multispectral imagers compatible with the detector dewar." In SPIE Defense, Security, and Sensing, edited by Bjørn F. Andresen, Gabor F. Fulop, and Paul R. Norton. SPIE, 2011. http://dx.doi.org/10.1117/12.883904.

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Lin, Yu, Ningfang Liao, Xinquan Wang, Deqi Cui, Minyong Liang, and Yongdao Luo. "Simultaneous acquisition of hyper-spectral image using the computed tomography imaging interferometer." In International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Tianxu Zhang, Carl A. Nardell, Duane D. Smith, and Hangqing Lu. SPIE, 2007. http://dx.doi.org/10.1117/12.750221.

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Song, Rui, Shengping Xia, and Jianjun Liu. "RSOM tree and class specific hyper graph based distributed image retrieval." In Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Jianguo Liu, Kunio Doi, Aaron Fenster, and S. C. Chan. SPIE, 2009. http://dx.doi.org/10.1117/12.832355.

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Gilchrist, John R., Christopher Durell, and Torbjorn Skauli. "IEEE P4001: progress update towards an international standard for push-broom hyper-spectral imagers." In Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVII, edited by David W. Messinger and Miguel Velez-Reyes. SPIE, 2021. http://dx.doi.org/10.1117/12.2588466.

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Reports on the topic "HYPER/MULTISPECTRAL IMAGERY"

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Burks, Thomas F., Victor Alchanatis, and Warren Dixon. Enhancement of Sensing Technologies for Selective Tree Fruit Identification and Targeting in Robotic Harvesting Systems. United States Department of Agriculture, October 2009. http://dx.doi.org/10.32747/2009.7591739.bard.

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Abstract:
The proposed project aims to enhance tree fruit identification and targeting for robotic harvesting through the selection of appropriate sensor technology, sensor fusion, and visual servo-control approaches. These technologies will be applicable for apple, orange and grapefruit harvest, although specific sensor wavelengths may vary. The primary challenges are fruit occlusion, light variability, peel color variation with maturity, range to target, and computational requirements of image processing algorithms. There are four major development tasks in original three-year proposed study. First, spectral characteristics in the VIS/NIR (0.4-1.0 micron) will be used in conjunction with thermal data to provide accurate and robust detection of fruit in the tree canopy. Hyper-spectral image pairs will be combined to provide automatic stereo matching for accurate 3D position. Secondly, VIS/NIR/FIR (0.4-15.0 micron) spectral sensor technology will be evaluated for potential in-field on-the-tree grading of surface defect, maturity and size for selective fruit harvest. Thirdly, new adaptive Lyapunov-basedHBVS (homography-based visual servo) methods to compensate for camera uncertainty, distortion effects, and provide range to target from a single camera will be developed, simulated, and implemented on a camera testbed to prove concept. HBVS methods coupled with imagespace navigation will be implemented to provide robust target tracking. And finally, harvesting test will be conducted on the developed technologies using the University of Florida harvesting manipulator test bed. During the course of the project it was determined that the second objective was overly ambitious for the project period and effort was directed toward the other objectives. The results reflect the synergistic efforts of the three principals. The USA team has focused on citrus based approaches while the Israeli counterpart has focused on apples. The USA team has improved visual servo control through the use of a statistical-based range estimate and homography. The results have been promising as long as the target is visible. In addition, the USA team has developed improved fruit detection algorithms that are robust under light variation and can localize fruit centers for partially occluded fruit. Additionally, algorithms have been developed to fuse thermal and visible spectrum image prior to segmentation in order to evaluate the potential improvements in fruit detection. Lastly, the USA team has developed a multispectral detection approach which demonstrated fruit detection levels above 90% of non-occluded fruit. The Israel team has focused on image registration and statistical based fruit detection with post-segmentation fusion. The results of all programs have shown significant progress with increased levels of fruit detection over prior art.
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