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Relevant bibliographies by topics / Hyperspectral signature

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Academic literature on the topic 'Hyperspectral signature'

Author: Grafiati

Published: 25 May 2024

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Journal articles on the topic "Hyperspectral signature"

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Papp, Adam, Julian Pegoraro, Daniel Bauer, Philip Taupe, Christoph Wiesmeyr, and Andreas Kriechbaum-Zabini. "Automatic Annotation of Hyperspectral Images and Spectral Signal Classification of People and Vehicles in Areas of Dense Vegetation with Deep Learning." Remote Sensing 12, no. 13 (2020): 2111. http://dx.doi.org/10.3390/rs12132111.

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Despite recent advances in image and video processing, the detection of people or cars in areas of dense vegetation is still challenging due to landscape, illumination changes and strong occlusion. In this paper, we address this problem with the use of a hyperspectral camera—installed on the ground or possibly a drone—and detection based on spectral signatures. We introduce a novel automatic method for annotating spectral signatures based on a combination of state-of-the-art deep learning methods. After we collected millions of samples with our method, we used a deep learning approach to train

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Wang, Jing. "Progressive coding for hyperspectral signature characterization." Optical Engineering 45, no. 9 (2006): 097002. http://dx.doi.org/10.1117/1.2353113.

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Gromov, V. P., L. I. Lebedev, and V. E. Turlapov. "Analysis and object markup of hyperspectral images for machine learning methods." Information Technology and Nanotechnology, no. 2391 (2019): 309–17. http://dx.doi.org/10.18287/1613-0073-2019-2391-309-317.

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The development of the nominal sequence of steps for analyzing the HSI proposed by Landgrebe, which is necessary in the context of the appearance of reference signature libraries for environmental monitoring, is discussed. The approach is based on considering the HSI pixel as a signature that stores all spectral features of an object and its states, and the HSI as a whole - as a two-dimensional signature field. As a first step of the analysis, a procedure is proposed for detecting a linear dependence of signatures by the magnitude of the Pearson correlation coefficient. The main apparatus of a

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Hartfield, Kyle, Jeffrey K. Gillan, Cynthia L. Norton, Charles Conley, and Willem J. D. van Leeuwen. "A Novel Spectral Index to Identify Cacti in the Sonoran Desert at Multiple Scales Using Multi-Sensor Hyperspectral Data Acquisitions." Land 11, no. 6 (2022): 786. http://dx.doi.org/10.3390/land11060786.

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Accurate identification of cacti, whether seen as an indicator of ecosystem health or an invasive menace, is important. Technological improvements in hyperspectral remote sensing systems with high spatial resolutions make it possible to now monitor cacti around the world. Cacti produce a unique spectral signature because of their morphological and anatomical characteristics. We demonstrate in this paper that we can leverage a reflectance dip around 972 nm, due to cacti’s morphological structure, to distinguish cacti vegetation from non-cacti vegetation in a desert landscape. We also show the a

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Chang, Chein-I., Sumit Chakravarty, Hsian-Min Chen, and Yen-Chieh Ouyang. "Spectral derivative feature coding for hyperspectral signature analysis." Pattern Recognition 42, no. 3 (2009): 395–408. http://dx.doi.org/10.1016/j.patcog.2008.07.016.

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Kim, Sungho, Jungho Kim, Jinyong Lee, and Junmo Ahn. "AS-CRI: A New Metric of FTIR-Based Apparent Spectral-Contrast Radiant Intensity for Remote Thermal Signature Analysis." Remote Sensing 11, no. 7 (2019): 777. http://dx.doi.org/10.3390/rs11070777.

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Infrared signature analysis that considers both the target and background is fundamentally important to the development of target detection systems as well as in the design of ships for thermal stealth. This paper presents the analysis results of long-term infrared signature variations in terms of the apparent spectral-contrast radiant intensity measured using Fourier transform infrared (FTIR)-based hyperspectral images. A novel apparent spectral-contrast radiant intensity (AS-CRI) measure is proposed to evaluate the spectral infrared signature accurately at the sensor point of view. The spect

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MESSINGER, DAVID W., CARL SALVAGGIO, and NATALIE M. SINISGALLI. "DETECTION OF GASEOUS EFFLUENTS FROM AIRBORNE LWIR HYPERSPECTRAL IMAGERY USING PHYSICS-BASED SIGNATURES." International Journal of High Speed Electronics and Systems 17, no. 04 (2007): 801–12. http://dx.doi.org/10.1142/s0129156407004990.

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Detection of gaseous effluent plumes from airborne platforms provides a unique challenge to the remote sensing community. The measured signatures are a complicated combination of phenomenology including effects of the atmosphere, spectral characteristics of the background material under the plume, temperature contrast between the gas and the surface, and the concentration of the gas. All of these quantities vary spatially further complicating the detection problem. In complex scenes simple estimation of a “residual” spectrum may not be possible due to the variability in the scene background. A

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Honkavaara, E., T. Hakala, O. Nevalainen, et al. "GEOMETRIC AND REFLECTANCE SIGNATURE CHARACTERIZATION OF COMPLEX CANOPIES USING HYPERSPECTRAL STEREOSCOPIC IMAGES FROM UAV AND TERRESTRIAL PLATFORMS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 17, 2016): 77–82. http://dx.doi.org/10.5194/isprs-archives-xli-b7-77-2016.

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Light-weight hyperspectral frame cameras represent novel developments in remote sensing technology. With frame camera technology, when capturing images with stereoscopic overlaps, it is possible to derive 3D hyperspectral reflectance information and 3D geometric data of targets of interest, which enables detailed geometric and radiometric characterization of the object. These technologies are expected to provide efficient tools in various environmental remote sensing applications, such as canopy classification, canopy stress analysis, precision agriculture, and urban material classification. F

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Honkavaara, E., T. Hakala, O. Nevalainen, et al. "GEOMETRIC AND REFLECTANCE SIGNATURE CHARACTERIZATION OF COMPLEX CANOPIES USING HYPERSPECTRAL STEREOSCOPIC IMAGES FROM UAV AND TERRESTRIAL PLATFORMS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 17, 2016): 77–82. http://dx.doi.org/10.5194/isprsarchives-xli-b7-77-2016.

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Light-weight hyperspectral frame cameras represent novel developments in remote sensing technology. With frame camera technology, when capturing images with stereoscopic overlaps, it is possible to derive 3D hyperspectral reflectance information and 3D geometric data of targets of interest, which enables detailed geometric and radiometric characterization of the object. These technologies are expected to provide efficient tools in various environmental remote sensing applications, such as canopy classification, canopy stress analysis, precision agriculture, and urban material classification. F

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Miljković, V., and D. Gajski. "ADAPTATION OF INDUSTRIAL HYPERSPECTRAL LINE SCANNER FOR ARCHAEOLOGICAL APPLICATIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5 (June 15, 2016): 343–45. http://dx.doi.org/10.5194/isprs-archives-xli-b5-343-2016.

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The spectral characteristic of the visible light reflected from any of archaeological artefact is the result of the interaction of its surface illuminated by incident light. Every particular surface depends on what material it is made of and/or which layers put on it has its spectral signature. Recent archaeometry recognises this information as very valuable data to extend present documentation of artefacts and as a new source for scientific exploration. However, the problem is having an appropriate hyperspectral imaging system available and adopted for applications in archaeology. In this pap

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Dissertations / Theses on the topic "Hyperspectral signature"

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Mathur, Abhinav. "DIMENSIONALITY REDUCTION OF HYPERSPECTRAL SIGNATURES FOR OPTIMIZED DETECTION OF INVASIVE SPECIES." MSSTATE, 2003. http://sun.library.msstate.edu/ETD-db/theses/available/etd-07112003-160125/.

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The aim of this thesis is to investigate the use of hyperspectral reflectance signals for the discrimination of cogongrass (Imperata cylindrica) from other subtly different vegetation species. Receiver operating characteristics (ROC) curves are used to determine which spectral bands should be considered as candidate features. Multivariate statistical analysis is then applied to the candidate features to determine the optimum subset of spectral bands. Linear discriminant analysis (LDA) is used to compute the optimum linear combination of the selected subset to be used as a feature for classific

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Hemissi, Selim. "Modélisation multidimensionnelle de signature spectrale pour le démixage et la classification en imagerie hyperspectrale multi-temporelle." Télécom Bretagne, 2014. http://www.theses.fr/2014TELB0307.

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L'imagerie hyperspectrale constitue une technologie de pointe assez fructueuse pour une cartographie précise de la surface terrestre. En analysant les données, la plupart des approches classiques traitent chaque date indépendamment, sans considérer l'entremêlement temporel omniprésent dans la formation des signatures spectrales. Inéluctablement, les types hétérogènes d'occupation du sol manifestent des signatures spectrales chevauchantes à cause de la variabilité inter/intra saisonnière des propriétés spectrales. Pour y pallier, nous essayons de repenser l'hypothèse d'unicité de la signature s

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Sirois, Jean-Philippe. "Impact et suivi de la variabilité climatique sur la production viticole dans le sud du Québec à l’aide de la télédétection hyperspectrale." Mémoire, Université de Sherbrooke, 2015. http://hdl.handle.net/11143/6011.

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Au Québec, la viticulture commerciale ou artisanale n’a que 35 ans. Cependant, le Québec est soumis à de nombreuses pressions climatiques comme la présence de gel hâtif à l’automne et tardif au printemps. La période de croissance (la différence entre le dernier gel au printemps et le premier gel à l’automne) est donc très limitée par la présence du froid. Dans un contexte de réchauffement climatique, cette période de croissance sera portée à s’allonger. Une plus longue période de croissance pourrait inciter les vignerons à modifier leurs cépages ou à augmenter la superficie cultivée. Trois vig

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Rousseau, Sylvain. "Détection de points d'intérêts dans une image multi ou hyperspectral par acquisition compressée." Thesis, Poitiers, 2013. http://www.theses.fr/2013POIT2269/document.

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Les capteurs multi- et hyper-spectraux génèrent un énorme flot de données. Un moyende contourner cette difficulté est de pratiquer une acquisition compressée de l'objet multi- ethyper-spectral. Les données sont alors directement compressées et l'objet est reconstruitlorsqu'on en a besoin. L'étape suivante consiste à éviter cette reconstruction et à travaillerdirectement avec les données compressées pour réaliser un traitement classique sur un objetde cette nature. Après avoir introduit une première approche qui utilise des outils riemannienspour effectuer une détection de contours dans une ima

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TENG, Chih-Heng, and 鄧至亨. "A spectral signature based non-local mean for hyperspectral image denoising." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/5wtabh.

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碩士<br>國立臺灣大學<br>電信工程學研究所<br>106<br>A new spectral signature method for hyperspectral images denoising named as hyperspectral non-local mean is proposed in this thesis. This method uses spectral information and spatial information to denoise hyperspectral images. Traditionally, spectral information and spatial information are used separately. Thus, there are two different groups of methods to denoise hyperspectral images, spatial algorithms and spectral algorithms. The spatial denoising methods such as smoothing filter, non-local mean and non-local Bayesian consider the correlation in an image.

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Feng, Siwei. "Wavelet-Based Non-Homogeneous Hidden Markov Chain Model For Hyperspectral Signature Classification." 2015. https://scholarworks.umass.edu/masters_theses_2/145.

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Hyperspectral signature classification is a kind of quantitative analysis approach for hyperspectral imagery which performs detection and classification of the constituent materials at pixel level in the scene. The classification procedure can be operated directly on hyperspectral data or performed by using some features extracted from corresponding hyperspectral signatures containing information like signature energy or shape. In this paper, we describe a technique that applies non-homogeneous hidden Markov chain (NHMC) models to hyperspectral signature classification. The basic idea is to us

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Hoffman, Forrest McCoy. "Analysis of reflected spectral signatures and detection of geophysical disturbance using hyperspectral imagery." 2004. http://etd.utk.edu/2004/HoffmanForrest.pdf.

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Thesis (M.S.)--University of Tennessee, Knoxville, 2004.<br>Title from title page screen (viewed Jan. 14, 2005). Thesis advisor: William E. Blass. Document formatted into pages (xi, 197 p. : ill. (some col.), maps)). Vita. Includes bibliographical references (p. 81-85).

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HSIEH, MINGCHE, and 謝明哲. "Study on the Modeling and Classification of the Mixed Pixel Analysis on Vegetation Hyperspectral Signatures." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/63526429667807224219.

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碩士<br>國立嘉義大學<br>森林暨自然資源學系研究所<br>99<br>Accurate measurement and characterization of fluctuations in the remote sensing data from satellite, airborne or in situ measurement. The adjacency effect increases the reflection of the target pixel from nearby pixels and path scattering. When substances with different spectral properties in the same pixel within the time, there will be mixed pixel. Mixed pixel is not entirely belong to a particular surface features, in order to make image classification more precise, It is necessary to divide into a variety of features in the percentage of pixel. There a

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Books on the topic "Hyperspectral signature"

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

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Ponder, Henley J., and U.S. Army Engineer Topographic Laboratories., eds. Hyperspectral signatures (400 to 2500 nm) of vegetation, minerals, soils, rocks, and cultural features: Laboratory and field measurements. U.S. Army Corps of Engineers, Engineer Topographic Laboratories, 1990.

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Book chapters on the topic "Hyperspectral signature"

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Chang, Chein-I. "Target Signature-Constrained Mixed Pixel Classification (TSCMPC): LCMV Classifiers." In Hyperspectral Imaging. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9170-6_11.

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Chang, Chein-I. "Target Signature-Constrained Subpixel Detection: Linearly Constrained Minimum Variance (LCMV)." In Hyperspectral Imaging. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9170-6_4.

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Chang, Chein-I. "Target Signature-Constrained Mixed Pixel Classification (TSCMPC): Linearly Constrained Discriminant Analysis (LCDA)." In Hyperspectral Imaging. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9170-6_12.

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Leshem, Guy, and Menachem Domb. "Face Authentication Using Image Signature Generated from Hyperspectral Inner Images." In Advances in Intelligent Systems and Computing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0637-6_9.

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Appice, Annalisa, and Pietro Guccione. "Exploiting Spatial Correlation of Spectral Signature for Training Data Selection in Hyperspectral Image Classification." In Discovery Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46307-0_19.

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Patil, Trunal, Claudia Pagano, Roberto Marani, Tiziana D’Orazio, Giacomo Copani, and Irene Fassi. "Hyperspectral Imaging for Non-destructive Testing of Composite Materials and Defect Classification." In Lecture Notes in Mechanical Engineering. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18326-3_39.

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AbstractCarbon fiber composite materials are intensively used in many manufacturing domains such as aerospace, aviation, marine, automation and civil industries due to their excellent strength, corrosion resistance, and lightweight properties. However, their increased use requires a conscious awareness of their entire life cycle and not only of their manufacturing. Therefore, to reduce waste and increase sustainability, reparation, reuse, or recycling are recommended in case of defects and wear. This can be largely improved with reliable and efficient non-destructive defect detection techniques; those are able to identify damages automatically for quality control inspection, supporting the definition of the best circular economy options. Hyperspectral imaging techniques provide unique features for detecting physical and chemical alterations of any material and, in this study, it is proposed to identify the constitutive material and classify local defects of composite specimens. A Middle Wave Infrared Hyperspectral Imaging (MWIR-HSI) system, able to capture spectral signatures of the specimen surfaces in a range of wavelengths between 2.6757 and 5.5056 µm, has been used. The resulting signatures feed a deep neural network with three convolutional layers that filter the input and isolate data-driven features of high significance. A complete experimental case study is presented to validate the methodology, leading to an average classification accuracy of 93.72%. This opens new potential opportunities to enable sustainable life cycle strategies for carbon fiber composite materials.

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Turra, Giovanni, Simone Arrigoni, and Alberto Signoroni. "CNN-Based Identification of Hyperspectral Bacterial Signatures for Digital Microbiology." In Image Analysis and Processing - ICIAP 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68548-9_46.

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Carmona-Zuluaga, Pablo, Maria C. Torres-Madronero, Manuel Goez, Tatiana Rondon, Manuel Guzman, and Maria Casamitjana. "Abiotic Maize Stress Detection Using Hyperspectral Signatures and Band Selection." In Smart Technologies, Systems and Applications. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-32213-6_35.

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"Binary Coding for Spectral Signatures." In Hyperspectral Data Processing. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118269787.ch24.

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"Vector Coding for Hyperspectral Signatures." In Hyperspectral Data Processing. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118269787.ch25.

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Conference papers on the topic "Hyperspectral signature"

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Morgan, Seldon O., Richard B. Gomez, and William E. Roper. "Squeezed signature analysis hyperspectral classification." In AeroSense 2003, edited by Nickolas L. Faust and William E. Roper. SPIE, 2003. http://dx.doi.org/10.1117/12.502414.

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Cathcart, J. Michael, Robert V. Worrall, and Daniel P. Cash. "Hyperspectral signature modeling for terrain backgrounds." In Defense and Security Symposium, edited by Wendell R. Watkins and Dieter Clement. SPIE, 2006. http://dx.doi.org/10.1117/12.666478.

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Vyas, Saurabh, Amit Banerjee, Luis Garza, Sewon Kang, and Philippe Burlina. "Hyperspectral signature analysis of skin parameters." In SPIE Medical Imaging, edited by Carol L. Novak and Stephen Aylward. SPIE, 2013. http://dx.doi.org/10.1117/12.2001428.

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Knaeps, Els, and Mehrdad Moshtaghi. "Evaluating the hyperspectral signature of marine plastics." In Hyperspectral Imaging and Sounding of the Environment. OSA, 2021. http://dx.doi.org/10.1364/hise.2021.htu2c.5.

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Settouti, Nesma, Olga Assainova, Nadine Abdallah Saab, and Marwa El Bouz. "Automated Hyperspectral Apple Variety Identification Based on Patch-wise Classification." In Applied Industrial Spectroscopy. Optica Publishing Group, 2023. http://dx.doi.org/10.1364/ais.2023.jw2a.28.

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Manual apple sorting is costly and subjective. We investigate using VNIR hyperspectral imaging for an efficient and objective solution. Our study presents a patch-wise classification approach for automatic recognition of apple varieties using their hyperspectral signature.

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Ozdemir, Okan Bilge, Hilal Soydan, Yasemin Yardimci Cetin, and H. Sebnem Duzgun. "Signature based vegetation detection on hyperspectral images." In 2015 23th Signal Processing and Communications Applications Conference (SIU). IEEE, 2015. http://dx.doi.org/10.1109/siu.2015.7130392.

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Chakravarty, Sumit, and Chein-I. Chang. "Block truncation signature coding for hyperspectral analysis." In Optical Engineering + Applications, edited by Sylvia S. Shen and Paul E. Lewis. SPIE, 2008. http://dx.doi.org/10.1117/12.796711.

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Pereira, Wellesley, David Less, Leonard Rodriguez, Allen Curran, Uri Bernstein, and Yit-Tsi Kwan. "Hyperspectral extensions in the MuSES signature code." In SPIE Defense and Security Symposium, edited by Dawn A. Trevisani. SPIE, 2008. http://dx.doi.org/10.1117/12.783933.

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Shah, Dharambhai, Y. N. Trivedi, and Tanish Zaveri. "Non-Linear Spectral Unmixing: A Case Study On Mangalore Aviris-Ng Hyperspectral Data." In 2020 IEEE Bombay Section Signature Conference (IBSSC). IEEE, 2020. http://dx.doi.org/10.1109/ibssc51096.2020.9332215.

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Seyfioglu, Mehmet Saygin, Seyma Bayindir, and Sevgi Zubeyde Gurbuz. "Automatic spectral signature extraction for hyperspectral target detection." In IGARSS 2015 - 2015 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2015. http://dx.doi.org/10.1109/igarss.2015.7326815.

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Reports on the topic "Hyperspectral signature"

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Chang, Chein-I., Jing Wang, Chein-Chi Chang, and Chinsu Lin. Progressive Coding for Hyperspectral Signature Characterization. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada455705.

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Pokrzywinski, Kaytee, Cliff Morgan, Scott Bourne, Molly Reif, Kenneth Matheson, and Shea Hammond. A novel laboratory method for the detection and identification of cyanobacteria using hyperspectral imaging : hyperspectral imaging for cyanobacteria detection. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40966.

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To assist US Army Corps of Engineers resource managers in monitoring for cyanobacteria bloom events, a laboratory method using hyperspectral imaging has been developed. This method enables the rapid detection of cyanobacteria in large volumes and has the potential to be transitioned to aerial platforms for field deployment. Prior to field data collection, validation of the technology in the laboratory using monocultures was needed. This report describes the development of the detection method using hyperspectral imaging and the stability/reliability of these signatures for identification purpo

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White, H. P., L. Sun, K. Staenz, R. A. Fernandes, and C. Champagne. Determining the Contribution of Shaded Elements of a Canopy to Remotely Sensed Hyperspectral Signatures. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2002. http://dx.doi.org/10.4095/219961.

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Lesser, Michael P. Coastal Benthic Optical Properties (CoBOP) of Coral Reef Environments: Small Scale Fluorescent Optical Signatures and Hyperspectral Remote Sensing of Coral Reef Habitats. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada627969.

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Lesser, Michael P. Coastal Benthic Optical Properties (CoBOP) of Coral Reef Environments: Small Scale Fluorescent Optical Signatures and Hyperspectral Remote Sensing of Coral Reef Habitats. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada628422.

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Budkewitsch, P., K. Staenz, J. Secker, A. Rencz, and D. Sangster. Spectral Signatures of Carbonate Rocks Surrounding the Nanisivik MVT Zn-Pb Mine and Implications of Hyperspectral Imaging for Exploration in Arctic Environments. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2000. http://dx.doi.org/10.4095/219736.

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Hodul, M., H. P. White, and A. Knudby. A report on water quality monitoring in Quesnel Lake, British Columbia, subsequent to the Mount Polley tailings dam spill, using optical satellite imagery. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/330556.

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In the early morning on the 4th of August 2014, a tailings dam near Quesnel, BC burst, spilling approximately 25 million m3 of runoff containing heavy metal elements into nearby Quesnel Lake (Byrne et al. 2018). The runoff slurry, which included lead, arsenic, selenium, and vanadium spilled through Hazeltine Creek, scouring its banks and picking up till and forest cover on the way, and ultimately ended up in Quesnel Lake, whose water level rose by 1.5 m as a result. While the introduction of heavy metals into Quesnel Lake was of environmental concern, the additional till and forest cover scour

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