Results 21 to 30 of about 11,136 (312)
Multi-resolution terrestrial hyperspectral dataset for spectral unmixing problems
Data in Brief, 2022 Recent developments in the miniaturization of hyperspectral imaging sensors have given rise to the increased use of hyperspectral imagery as the primary data for evaluating spectral unmixing algorithms in applications such as industrial quality control ...
C.V.S.S. Manohar Kumar +3 more
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Classification of hyperspectral imagery on embedded Grassmannians [PDF]
2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2014 We propose an approach for capturing the signal variability in hyperspectral imagery using the framework of the Grassmann manifold. Labeled points from each class are sampled and used to form abstract points on the Grassmannian. The resulting points on the Grassmannian have representations as orthonormal matrices and as such do not reside in Euclidean ...
Sofya Chepushtanova, Michael Kirby
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Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery [PDF]
, 2009 This paper studies a fully Bayesian algorithm for endmember extraction and abundance estimation for hyperspectral imagery. Each pixel of the hyperspectral image is decomposed as a linear combination of pure endmember spectra following the linear mixing ...
Moussaoui, Saïd +10 more
core +1 more source
Retrieval of Sediment Filling Factor in a Salt Panne from Multi-View Hyperspectral Imagery
Remote Sensing, 2020 This work describes a study using multi-view hyperspectral imagery to retrieve sediment filling factor through inversion of a modified version of the Hapke radiative transfer model.
Rehman S. Eon +4 more
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Visualization of hyperspectral imagery [PDF]
SPIE Proceedings, 2007 We developed four new techniques to visualize hyper spectral image data for man-in-the-loop target detection. The methods respectively: (1) display the subsequent bands as a movie (movie), (2) map the data onto three channels and display these as a colour image (colour), (3) display the correlation between the pixel signatures and a known target ...
Hogervorst, M.A., Bijl, P., Toet, A.
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Customizing kernel functions for SVM-based hyperspectral image classification [PDF]
, 2008 Previous research applying kernel methods such as support vector machines (SVMs) to hyperspectral image classification has achieved performance competitive with the best available algorithms.
Damper, R. I. +7 more
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TREE SPECIES CLASSIFICATION BASED ON 3D SPECTRAL POINT CLOUDS AND ORTHOMOSAICS ACQUIRED BY SNAPSHOT HYPERSPECTRAL UAS SENSOR [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019 In recent years, there has been a growing number of small hyperspectral sensors suitable for deployment on unmanned aerial systems (UAS. The introduction of the hyperspectral snapshot sensor provides interesting opportunities for acquisition of three ...
C. Iseli, A. Lucieer
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Compared with land surface temperature (LST) and land surface emissivity (LSE) retrieval from single-band or multispectral thermal infrared (TIR) data, TIR hyperspectral imagery allows us to obtain accurate LST and LSE through the use of an automatic ...
Lyuzhou Gao +4 more
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An Advanced Spectral–Spatial Classification Framework for Hyperspectral Imagery Based on DeepLab v3+
Applied Sciences, 2021 DeepLab v3+ neural network shows excellent performance in semantic segmentation. In this paper, we proposed a segmentation framework based on DeepLab v3+ neural network and applied it to the problem of hyperspectral imagery classification (HSIC).
Yifan Si +7 more
doaj +1 more source
Supervised nonlinear spectral unmixing using a post-nonlinear mixing model for hyperspectral imagery [PDF]
, 2011 This paper presents a nonlinear mixing model for hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are nonlinear functions of pure spectral components contaminated by an additive white Gaussian noise.
Altmann, Yoann +9 more
core +1 more source