Relevant bibliographies by topics / Hyperspectral remote sensing

Contents

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

Academic literature on the topic 'Hyperspectral remote sensing'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 September 2023

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Journal articles on the topic "Hyperspectral remote sensing"

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Liu, Yun, and Jia-Bao Liu. "Abnormal Target Detection Method in Hyperspectral Remote Sensing Image Based on Convolution Neural Network." Computational Intelligence and Neuroscience 2022 (May 17, 2022): 1–8. http://dx.doi.org/10.1155/2022/9223552.

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Abnormal target detection in hyperspectral remote sensing image is one of the hotspots in image research. The image noise generated in the detection process will lead to the decline of the quality of hyperspectral remote sensing image. In view of this, this paper proposes an abnormal target detection method of hyperspectral remote sensing image based on the convolution neural network. Firstly, the deep residual learning network model has been used to remove the noise in hyperspectral remote sensing image. Secondly, the spatial and spectral features of hyperspectral remote sensing images were used to optimize the clustering dictionary, and then the image segmentation containing target information is completed. Finally, the image was input into the deep convolution neural network with a dual classifier, and the network detects the abnormal target in the image. The test results of this algorithm show that the structural similarity of the denoised image is higher than 0.86, which shows that this method has good noise reduction performance, image details will not damage, segmentation effect is good, and it can obtain high-definition target image information and accurately detect abnormal targets in the image.
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Zhang, Jing, Qianlan Zhou, Li Zhuo, Wenhao Geng, and Suyu Wang. "A CBIR System for Hyperspectral Remote Sensing Images Using Endmember Extraction." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 04 (February 2, 2017): 1752001. http://dx.doi.org/10.1142/s0218001417520012.

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With the rapid development of remote sensing technology, searching the similar image is a challenge for hyperspectral remote sensing image processing. Meanwhile, the dramatic growth in the amount of hyperspectral remote sensing data has stimulated considerable research on content-based image retrieval (CBIR) in the field of remote sensing technology. Although many CBIR systems have been developed, few studies focused on the hyperspectral remote sensing images. A CBIR system for hyperspectral remote sensing image using endmember extraction is proposed in this paper. The main contributions of our method are that: (1) the endmembers as the spectral features are extracted from hyperspectral remote sensing image by improved automatic pixel purity index (APPI) algorithm; (2) the spectral information divergence and spectral angle match (SID–SAM) mixed measure method is utilized as a similarity measurement between hyperspectral remote sensing images. At last, the images are ranked with descending and the top-[Formula: see text] retrieved images are returned. The experimental results on NASA datasets show that our system can yield a superior performance.
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Apan, A., and S. Phinn. "Special feature –hyperspectral remote sensing." Journal of Spatial Science 51, no. 2 (December 2006): 47–48. http://dx.doi.org/10.1080/14498596.2006.9635080.

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Im, Jungho, and John R. Jensen. "Hyperspectral Remote Sensing of Vegetation." Geography Compass 2, no. 6 (November 2008): 1943–61. http://dx.doi.org/10.1111/j.1749-8198.2008.00182.x.

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Huang, Shi-Qi, Wen-Sheng Wu, Li-Ping Wang, and Xiang-Yang Duan. "Methods of removal wide-stripe noise in short-wave infrared hyperspectral remote sensing image." Sensor Review 39, no. 1 (January 21, 2019): 17–23. http://dx.doi.org/10.1108/sr-03-2017-0039.

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Purpose This paper aims to study the removal of wide-stripe noise in hyperspectral remote sensing images. There is a great deal of stripe noises in short-wave infrared hyperspectral remote sensing image, especially wide-stripe noise, which brings great challenge to the interpretation and application of hyperspectral images. Design/methodology/approach To remove the noise and to reduce the impact based on in-depth study of the mechanism of the stripe noise generation and its distribution characteristics, this paper proposed two statistical local processing and moment matching algorithms for the elimination of wide-stripe noise, namely, the gradient mean moment matching (GMMM) algorithm and the gradient interpolation moment matching (GIMM) algorithm. Findings The experiments were carried out with the practical short-wave infrared hyperspectral image data and good experiment results were obtained. Experiments show that both can reduce the impact of wide-stripe noise, and the filtering effect and the application range of the GIMM algorithm is better than that of the GMMM algorithm. Originality/value Using new methods to deal with the hyperspectral remote sensing images, it can effectively improve the quality of hyperspectral images and improve their utilization efficiency and value.
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Kwan, Chiman. "Remote Sensing Performance Enhancement in Hyperspectral Images." Sensors 18, no. 11 (October 23, 2018): 3598. http://dx.doi.org/10.3390/s18113598.

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Hyperspectral images with hundreds of spectral bands have been proven to yield high performance in material classification. However, despite intensive advancement in hardware, the spatial resolution is still somewhat low, as compared to that of color and multispectral (MS) imagers. In this paper, we aim at presenting some ideas that may further enhance the performance of some remote sensing applications such as border monitoring and Mars exploration using hyperspectral images. One popular approach to enhancing the spatial resolution of hyperspectral images is pansharpening. We present a brief review of recent image resolution enhancement algorithms, including single super-resolution and multi-image fusion algorithms, for hyperspectral images. Advantages and limitations of the enhancement algorithms are highlighted. Some limitations in the pansharpening process include the availability of high resolution (HR) panchromatic (pan) and/or MS images, the registration of images from multiple sources, the availability of point spread function (PSF), and reliable and consistent image quality assessment. We suggest some proactive ideas to alleviate the above issues in practice. In the event where hyperspectral images are not available, we suggest the use of band synthesis techniques to generate HR hyperspectral images from low resolution (LR) MS images. Several recent interesting applications in border monitoring and Mars exploration using hyperspectral images are presented. Finally, some future directions in this research area are highlighted.
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Zhao, Rui, and Shihong Du. "An Encoder–Decoder with a Residual Network for Fusing Hyperspectral and Panchromatic Remote Sensing Images." Remote Sensing 14, no. 9 (April 20, 2022): 1981. http://dx.doi.org/10.3390/rs14091981.

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For many urban studies it is necessary to obtain remote sensing images with high hyperspectral and spatial resolution by fusing the hyperspectral and panchromatic remote sensing images. In this article, we propose a deep learning model of an encoder–decoder with a residual network (EDRN) for remote sensing image fusion. First, we combined the hyperspectral and panchromatic remote sensing images to circumvent the independence of the hyperspectral and panchromatic image features. Second, we established an encoder–decoder network for extracting representative encoded and decoded deep features. Finally, we established residual networks between the encoder network and the decoder network to enhance the extracted deep features. We evaluated the proposed method on six groups of real-world hyperspectral and panchromatic image datasets, and the experimental results confirmed the superior performance of the proposed method versus six other methods.
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Yamano, Hiroya, Masayuki Tamura, Yoshimitsu Kunii, and Michio Hidaka. "Hyperspectral Remote Sensing and Radiative Transfer Simulation as a Tool for Monitoring Coral Reef Health." Marine Technology Society Journal 36, no. 1 (March 1, 2002): 4–13. http://dx.doi.org/10.4031/002533202787914205.

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Recent advances in the remote sensing of coral reefs include hyperspectral remote sensing and radiative transfer modeling. Hyperspectral data can be regarded as continuous and the derivative spectroscopy is effective for extracting coral reef components, including sand, macroalgae, and healthy, bleached, recently dead, and old dead coral. Radiative transfer models are effective for feasibility studies of satellite or airborne remote sensing. Using these techniques, we simulate and analyze the apparent reflectance of coral reef benthic features associated with bleaching events, obtained by hyperspectral sensors on various platforms (ROV, boat, airplane, and satellite), and suggest that the coral reef health on reef flats can be discriminated precisely. Remote sensing using hyperspectral sensors should significantly contribute to mapping and monitoring coral reef health.
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Che, Yong Fei, Ying Jun Zhao, and Wen Huan Wu. "Discussion on Development of Mineral Identification System Based on IDL." Advanced Materials Research 971-973 (June 2014): 1607–10. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.1607.

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The traditional data processing and analysis method of remote sensing image processing system cannot meet the hyperspectral remote sensing mass data processing and the need of practical application in mineral resources exploration. By studying the systematical analysis and key technology on the hyperspectral mineral information identification module, and analyzing and thinking about the relevant theoretical methods and technical process, carried out the development of hyperspectral mineral information identification module based on IDL and integrated with ENVI software, providing the basic support platform for hyperspectral remote sensing mineral resources exploration. Meanwhile, the existing problems were discussed from the spectral characteristics mechanism analysis of rock and the hyperspectral mineral identification optimization algorithms.
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Terentev, Anton, Vladimir Badenko, Ekaterina Shaydayuk, Dmitriy Emelyanov, Danila Eremenko, Dmitriy Klabukov, Alexander Fedotov, and Viktor Dolzhenko. "Hyperspectral Remote Sensing for Early Detection of Wheat Leaf Rust Caused by Puccinia triticina." Agriculture 13, no. 6 (June 2, 2023): 1186. http://dx.doi.org/10.3390/agriculture13061186.

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Early crop disease detection is one of the most important tasks in plant protection. The purpose of this work was to evaluate the early wheat leaf rust detection possibility using hyperspectral remote sensing. The first task of the study was to choose tools for processing and analyze hyperspectral remote sensing data. The second task was to analyze the wheat leaf biochemical profile by chromatographic and spectrophotometric methods. The third task was to discuss a possible relationship between hyperspectral remote sensing data and the results from the wheat leaves, biochemical profile analysis. The work used an interdisciplinary approach, including hyperspectral remote sensing and data processing methods, as well as spectrophotometric and chromatographic methods. As a result, (1) the VIS-NIR spectrometry data analysis showed a high correlation with the hyperspectral remote sensing data; (2) the most important wavebands for disease identification were revealed (502, 466, 598, 718, 534, 766, 694, 650, 866, 602, 858 nm). An early disease detection accuracy of 97–100% was achieved from fourth dai (day/s after inoculation) using SVM.
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Dissertations / Theses on the topic "Hyperspectral remote sensing"

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Kam, F. "Classification techniques for hyperspectral remote sensing." Thesis, Department of Informatics and Sensors, 2011. http://dspace.lib.cranfield.ac.uk/handle/1826/6163.

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This study concerns with classification techniques in high dimensional space such as that of Hyperspectral Imaging (HSI) data sets, with objectives of understanding the strength and weakness of various classifiers and at the same time to study how their performances can be assessed particularly when there is an absence of ground truth target map in the data set. The thesis summaries the work that carried out during the course of this study and it encompasses a brief survey of machine learning and classification theories, an outline of the HSI instrumentations, data sets that collected in the study and classification analysis. It is found that the supervised classifiers such as the Maximum Likelihood (QD) and the Mahalanobis Distance (FD) classifiers, especially when they are coupled with techniques like Regularised Discriminant Analysis (RDA) or leave-one-out covariance estimations (LOOC), have demonstrated excellent performances comparable to that of the more complicated and computational costly classifiers like the Support Vector Machine (SVM). This work has also revealed that separability measures such as the Total Transformed Divergence (TTD) and Total Jeffries-Matusita Distance (TJM) can be an invaluable method for assessing the goodness of classification in principle. However, the present methods for the evaluation of the separability measures are insufficient for achieving this goal and further work in this area is needed. This study has also confirmed the effectiveness for using RDA and LOOC techniques for a better estimation of the covariance when the sample size is small, ie when the sample size per class to band ratio is less than 100. Through team work this study has contributed partially a number of publications in the area of hyperspectral imaging and machine visions.
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Cisz, Adam. "Performance comparison of hyperspectral target detection algorithms /." Online version of thesis, 2006. https://ritdml.rit.edu/dspace/handle/1850/3020.

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Thulin, Susanne Maria, and smthulin@telia com. "Hyperspectral Remote Sensing of Temperate Pasture Quality." RMIT University. Mathematical and Geospatial Sciences, 2009. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20090507.163006.

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This thesis describes the research undertaken for the degree of Doctor of Philosophy, testing the hypothesis that spectrometer data can be used to establish usable relationships for prediction of pasture quality attributes. The research data consisted of reflectance measurements of various temperate pasture types recorded at four different times (years 2000 to 2002), recorded by three hyperspectral sensors, the in situ ASD, the airborne HyMap and the satellite-borne Hyperion. Corresponding ground-based pasture samples were analysed for content of chlorophyll, water, crude protein, digestibility, lignin and cellulose at three study sites in rural Victoria, Australia. This context was used to evaluate effects of sensor differences, data processing and enhancement, analytical methods and sample variability on the predictive capacity of derived prediction models. Although hyperspectral data analysis is being applied in many areas very few studies on temperate pastures have been conducted and hardly any encompass the variability and heterogeneity of these southern Australian examples. The research into the relationship between the spectrometer data and pasture quality attribute assays was designed using knowledge gained from assessment of other hyperspectral remote sensing and near-infrared spectroscopy research, including bio-chemical and physical properties of pastures, as well as practical issues of the grazing industries and carbon cycling/modelling. Processing and enhancement of the spectral data followed methods used by other hyperspectral researchers with modifications deemed essential to produce better relationships with pasture assay data. As many different methods are in use for the analysis of hyperspectral data several alternative approaches were investigated and evaluated to determine reliability, robustness and suitability for retrieval of temperate pasture quality attributes. The analyses employed included stepwise multiple linear regression (SMLR) and partial least squares regression (PLSR). The research showed that the spectral research data had a higher potential to be used for prediction of crude protein and digestibility than for the plant fibres lignin and cellulose. Spectral transformation such as continuum removal and derivatives enhanced the results. By using a modified approach based on sample subsets identified by a matrix of subjective bio-physical and ancillary data parameters, the performance of the models were enhanced. Prediction models from PLSR developed on ASD in situ spectral data, HyMap airborne imagery and Hyperion and corresponding pasture assays showed potential for predicting the two important pasture quality attributes crude protein and digestibility in hyperspectral imagery at a few quantised levels corresponding to levels currently used in commercial feed testing. It was concluded that imaging spectrometry has potential to offer synoptic, simultaneous and spatially continuous information valuable to feed based enterprises in temperate Victoria. The thesis provide a significant contribution to the field of hyperspectral remote sensing and good guidance for future hyperspectral researchers embarking on similar tasks. As the research is based on temperate pastures in Victoria, Australia, which are dominated by northern hemisphere species, the findings should be applicable to analysis of temperate pastures elsewhere, for example in Western Australia, New Zealand, South Africa, North America, Europe and northern Asia (China).
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Gao, Jincheng. "Canopy chlorophyll estimation with hyperspectral remote sensing." Diss., Manhattan, Kan. : Kansas State University, 2006. http://hdl.handle.net/2097/252.

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Jia, Xiuping Electrical Engineering Australian Defence Force Academy UNSW. "Classification techniques for hyperspectral remote sensing image data." Awarded by:University of New South Wales - Australian Defence Force Academy. School of Electrical Engineering, 1996. http://handle.unsw.edu.au/1959.4/38713.

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Hyperspectral remote sensing image data, such as that recorded by AVIRIS with 224 spectral bands, provides rich information on ground cover types. However, it presents new problems in machine assisted interpretation, mainly in long processing times and the difficulties of class training due to the low ratio of number of training samples to the number of bands. This thesis investigates feasible and efficient feature reduction and image classification techniques which are appropriate for hyperspectral image data. The study is reported in three parts. The first concerns a deterministic approach for hyperspectral data interpretation. Multigroup and multiple threshold spectral coding procedures, and associated techniques for spectral matching and classification, are proposed and tested. By coding on subgroups of bands using one or three thresholds, spectral searching and matching becomes simple, fast and free of the need for radiometric correction. Modifications of existing statistical techniques are proposed in the second part of the investigation A block-based maximum likelihood classification technique is developed. Several subgroups are formed from the complete set of spectral bands in the data, based on the properties of global correlation among the bands. Subgroups which are poorly correlated with each other are treated independently using conventional maximum likelihood classification. Experimental results demonstrate that, when using appropriate subgroup sizes, the new method provides a compromise among classification accuracy, processing time and available training pixels. Furthermore, a segmented, and possibly multi-layer, principal components transformation is proposed as a possible feature reduction technique prior to classification, and for effective colour display. The transformation is performed efficiently on each of the highly correlated subgroups of bands independently. Selected features from each transformed subgroup can be then transformed again to achieve a satisfactory data reduction ratio and to generate the three most significant components for colour display. Classification accuracy is improved and high quality colour image display is achieved in experiments using two AVIRIS data sets.
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Klonowski, Wojciech Mateusz. "Hyperspectral Remote Sensing Applied to Shallow Coastal Waters." Thesis, Curtin University, 2015. http://hdl.handle.net/20.500.11937/48821.

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A shallow water reflectance model was developed for application to optical remote sensing in highly diverse and complex coastal environments. A numerical inversion scheme, based on analytical parameterisation, was applied to airborne hyperspectral imagery collected over two regions of the Western Australian coastline; Jurien Bay and the Ningaloo Marine Park. Detailed maps of water quality, water depth and benthic cover classification were derived with a high degree of accuracy as compared to ground truth data.
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Bishoff, Josef P. "Target detection using oblique hyperspectral imagery : a domain trade study /." Online version of thesis, 2008. http://hdl.handle.net/1850/7834.

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Morman, Christopher Joseph. "Hyperspectral Target Detection Performance Modeling." University of Dayton / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1446587051.

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Lewis, Ryan H. "Topological & network theoretic approaches in hyperspectral remote sensing /." Online version of thesis, 2010. http://ritdml.rit.edu/handle/1850/12274.

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Hay, Lorraine. "Variations in modelled and measured hyperspectral remote sensing reflectance." Thesis, University of Strathclyde, 2006. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=21610.

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Measurements of hyperspectral surface reflectance, with a spectral range of 350-800nm and sampling interval of 3.3nm, were made in Scottish coastal waters, the Bristol Channel and the Atlantic Ocean. Analysis of the shape of these spectra by normalisation and differentiation revealed three prominent features: (1) the magnitude of the integral between 400-455nm, (2) the width of a trough occurring between 560-615nm, and (3) the peak to trough height between 660-750nm. The characteristics of these features were not determined by individual seawater constituents, but they proved useful as a tool for water type classification. The sign of the integral between 400-455nm discriminated between open ocean and coastal waters, and coastal sub-types could be distinguished by applying cluster analysis to the other three features. The hyperspectral data were integrated over appropriate bandwidths to generate multi-band surface reflectance values which were used to assess the performance of remote sensing algorithms in coastal water. All the chlorophyll algorithms tested (SeaWiFS OC4V4, MODIS Chlor_a_2 and Chlor_a_3, and MERIS OC4E) overestimated the values measured in situ. The MODIS algorithm for absorption by phytoplankton, αphyto(675), performed poorly, but the MODIS algorithm for the absorption by coloured dissolved organic material, αCDOM(400), provided accurate values of the absorption coefficient (R² = 0.91). Algorithm performance was improved when turbid stations, identified using cluster analysis, were removed. Hyperspectral radiometry was also used to investigate variations in chlorophyll fluorescence line height (FLH) with chlorophyll concentration, solar irradiation and seawater composition. FLH and chlorophyll α concentration were not correlated in the coastal waters sampled and variations in the photosynthetically available radiation (PAR), CDOM and suspended sediment concentrations affected the magnitude of FLH observed. A study of (FLH / Chl) under natural, fluctuating irradiances allowed the onset of adaptive non-photochemical quenching to be monitored in situ.
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Books on the topic "Hyperspectral remote sensing"

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Hyperspectral remote sensing. Bellingham, Wash: SPIE, 2012.

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Hyperspectral remote sensing of vegetation. Boca Raton: Taylor & Francis, 2012.

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1968-, Rajendran S., ed. Hyperspectral remote sensing & spectral signature applications. New Delhi: New India Pub. Agency, 2009.

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Borengasser, Marcus. Hyperspectral remote sensing: Principles and applications. Boca Raton: CRC Press, 2008.

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Bagheri, Sima. Hyperspectral Remote Sensing of Nearshore Water Quality. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-46949-2.

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Pedram, Ghamisi, ed. Spectral-spatial classififcation of hyperspectral remote sensing images. Boston: Artech House, 2015.

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S, Chavez Pat, Marino Carlo M, Schowengerdt Robert A, Society of Photo-optical Instrumentation Engineers., and Commission of the European Communities. Directorate-General for Science, Research, and Development., eds. Recent advances in remote sensing and hyperspectral remote sensing: 27-29 September 1994, Rome, Italy. Bellingham, Wash., USA: SPIE, 1994.

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S, Shen Sylvia, Society of Photo-optical Instrumentation Engineers., North American Remote Sensing Industries Association., and American Society of Photogrammetry, eds. Hyperspectral remote sensing and applications: 5-6 August 1996. Bellingham, Wash., USA: SPIE, 1996.

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Margaret, Kalacska, and Sánchez-Azofeifa Gerardo-Arturo, eds. Hyperspectral remote sensing of tropical and sub-tropical forests. Boca Raton: CRC Press, 2008.

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Giovanni, Motta, Rizzo Francesco, and Storer James A. 1953-, eds. Hyperspectral data compression. New York: Springer, 2005.

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Book chapters on the topic "Hyperspectral remote sensing"

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Gupta, Ravi Prakash. "Hyperspectral Sensing." In Remote Sensing Geology, 287–315. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05283-9_11.

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Ben-Dor, Eyal, Daniel Schläpfer, Antonio J. Plaza, and Tim Malthus. "Hyperspectral Remote Sensing." In Airborne Measurements for Environmental Research, 413–56. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2013. http://dx.doi.org/10.1002/9783527653218.ch8.

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Lee, Zhongping, and Kendall L. Carder. "Hyperspectral Remote Sensing." In Remote Sensing of Coastal Aquatic Environments, 181–204. Dordrecht: Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-3100-7_8.

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Marinelli, Daniele, Francesca Bovolo, and Lorenzo Bruzzone. "Hyperspectral Remote Sensing." In Encyclopedia of Mathematical Geosciences, 1–6. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-26050-7_155-1.

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Marinelli, Daniele, Francesca Bovolo, and Lorenzo Bruzzone. "Hyperspectral Remote Sensing." In Encyclopedia of Mathematical Geosciences, 625–30. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-85040-1_155.

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Pu, Ruiliang. "Overview of Hyperspectral Remote Sensing." In Hyperspectral Remote Sensing, 1–30. Boca Raton : Taylor & Francis, [2017]: CRC Press, 2017. http://dx.doi.org/10.1201/9781315120607-1.

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Pu, Ruiliang. "Field Spectrometers and Plant Biology Instruments for HRS." In Hyperspectral Remote Sensing, 31–64. Boca Raton : Taylor & Francis, [2017]: CRC Press, 2017. http://dx.doi.org/10.1201/9781315120607-2.

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Pu, Ruiliang. "Imaging Spectrometers, Sensors, Systems, and Missions." In Hyperspectral Remote Sensing, 65–100. Boca Raton : Taylor & Francis, [2017]: CRC Press, 2017. http://dx.doi.org/10.1201/9781315120607-3.

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Pu, Ruiliang. "Hyperspectral Image Radiometric Correction." In Hyperspectral Remote Sensing, 101–62. Boca Raton : Taylor & Francis, [2017]: CRC Press, 2017. http://dx.doi.org/10.1201/9781315120607-4.

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Pu, Ruiliang. "Hyperspectral Data Analysis Techniques." In Hyperspectral Remote Sensing, 163–228. Boca Raton : Taylor & Francis, [2017]: CRC Press, 2017. http://dx.doi.org/10.1201/9781315120607-5.

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Conference papers on the topic "Hyperspectral remote sensing"

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Nascimento, José M. P., and José M. Bioucas-Dias. "Blind hyperspectral unmixing." In Remote Sensing, edited by Lorenzo Bruzzone. SPIE, 2007. http://dx.doi.org/10.1117/12.738158.

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Smith, William L., Daniel K. Zhou, Henry E. Revercomb, Hung L. Huang, Poalo Antonelli, and Steven A. Mango. "Hyperspectral atmospheric sounding." In Remote Sensing, edited by Klaus Schaefer, Adolfo Comeron, Michel R. Carleer, and Richard H. Picard. SPIE, 2004. http://dx.doi.org/10.1117/12.515209.

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Nascimento, José M. P., and José M. Bioucas-Dias. "Unmixing hyperspectral intimate mixtures." In Remote Sensing, edited by Lorenzo Bruzzone. SPIE, 2010. http://dx.doi.org/10.1117/12.865118.

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Chang, Chein-I. "Progressive hyperspectral imaging." In SPIE Remote Sensing, edited by Bormin Huang and Antonio J. Plaza. SPIE, 2012. http://dx.doi.org/10.1117/12.979188.

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Sweet, James N., Mary H. Sharp, and James C. Granahan. "Hyperspectral analysis toolset." In Europto Remote Sensing, edited by Hiroyuki Fujisada, Joan B. Lurie, Alexander Ropertz, and Konradin Weber. SPIE, 2001. http://dx.doi.org/10.1117/12.417150.

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Figov, Zvi, Karni Wolowelsky, and Nitzan Goldberg. "Co-registration of hyperspectral bands." In Remote Sensing, edited by Lorenzo Bruzzone. SPIE, 2007. http://dx.doi.org/10.1117/12.737950.

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Schistad Solberg Asbjørn Berg, Anne, and Are F. C. Jensen. "Robust classification of hyperspectral images." In Remote Sensing, edited by Lorenzo Bruzzone. SPIE, 2007. http://dx.doi.org/10.1117/12.753095.

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DaltonIII, J. Brad, L. Jean Palmer-Moloney, Dana Rogoff, Chris Hlavka, Corinne Duncan, and Curtis Pehl. "Hyperspectral studies of hypersaline ecosystems." In Remote Sensing, edited by Charles R. BostaterJr. and Rosalia Santoleri. SPIE, 2005. http://dx.doi.org/10.1117/12.627438.

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Kacenjar, Steve T., Michael Hoffberg, and Patrick North. "Clutter characterization within segmented hyperspectral imagery." In Remote Sensing, edited by Lorenzo Bruzzone. SPIE, 2007. http://dx.doi.org/10.1117/12.737084.

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Aqdus, Syed A., William S. Hanson, and Jane Drummond. "Finding archaeological cropmarks: a hyperspectral approach." In Remote Sensing, edited by Manfred Ehlers and Ulrich Michel. SPIE, 2007. http://dx.doi.org/10.1117/12.738007.

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Reports on the topic "Hyperspectral remote sensing"

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Davis, Curtiss O. Airborne Hyperspectral Remote Sensing. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada631001.

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Davis, Curtiss O. Airborne Hyperspectral Remote Sensing. Fort Belvoir, VA: Defense Technical Information Center, September 2001. http://dx.doi.org/10.21236/ada625021.

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Maffione, Robert A. Hyperspectral Optical Properties, Remote Sensing, and Underwater Visibility. Fort Belvoir, VA: Defense Technical Information Center, August 2001. http://dx.doi.org/10.21236/ada627908.

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Maffione, Robert A. Hyperspectral Optical Properties, Remote Sensing, and Underwater Visibility. Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada626515.

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Bachmann, Charles M., Marcos J. Montes, Robert A. Fusina, Rong-Rong Li, Deric Gray, Daniel Korwan, Carl Gross, Christopher Jones, Krista Lee, and Jon Wende. Mariana Islands-Hyperspectral Airborne Remote Environmental Sensing Experiment 2010. Fort Belvoir, VA: Defense Technical Information Center, April 2012. http://dx.doi.org/10.21236/ada559525.

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Bachmann, Charles M., Robert A. Fusina, Marcos J. Montes, Rong-Rong Li, C. R. Nichols, John C. Fry, Patrick Woodward, Eric Hallenborg, Chris Parrish, and Jon Sellars. Hawaii-Hyperspectral Airborne Remote Environmental Sensing (HIHARES'09) Experiment. Fort Belvoir, VA: Defense Technical Information Center, March 2012. http://dx.doi.org/10.21236/ada559833.

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Deguise, J. C., M. McGovern, H. McNairn, and K. Staenz. Spatial High Resolution Crop Measurements with Airborne Hyperspectral Remote Sensing. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1998. http://dx.doi.org/10.4095/219371.

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McNairn, H., J. C. Deguise, A. Pacheco, J. Shang, and N. Rabe. Estimation of Crop Cover and Chlorophyll from Hyperspectral Remote Sensing. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2001. http://dx.doi.org/10.4095/219795.

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Pacheco, A., A. Bannari, J. C. Deguise, H. McNairn, and K. Staenz. Application of Hyperspectral Remote Sensing for LAI Estimation in Precision Farming. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2001. http://dx.doi.org/10.4095/219855.

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Champagne, C., K. Staenz, A. Bannari, H. P. White, J. C. Deguise, and H. McNairn. Estimation of Plant Water Content of Agricultural Canopies Using Hyperspectral Remote Sensing. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2002. http://dx.doi.org/10.4095/219955.

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