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Algorithm taxonomy for hyperspectral unmixing
SPIE Proceedings, 2000In this paper, we introduce a set of taxonomies that hierarchically organize and specify algorithms associated with hyperspectral unmixing. Our motivation is to collectively organize and relate algorithms in order to assess the current state-of-the-art in the field and to facilitate objective comparisons between methods.
Nirmal Keshava +3 more
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Feature Extraction-Based Hyperspectral Unmixing
2019Purpose Hyperspectral imaging belongs to a class of techniques called spectral imaging or spectral analysis. Due to the high dimensionality of hyperspectral cubes, it is a very difficult task to select few informative bands from original hyperspectral remote sensing images.
M. R. Vimala Devi, S. Kalaivani
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Unsupervised Unmixing of Hyperspectral Imagery
2006 49th IEEE International Midwest Symposium on Circuits and Systems, 2006This paper presents an approach for simultaneous determination of end members and their abundances in hyperspectral imagery using a constrained positive matrix factorization. The algorithm presented here solves the constrained PMF using Gauss-Seidel method.
Yahya M. Masalmah, Miguel Velez-Reyes
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KMNET for Hyperspectral Unmixing
2023 IEEE India Geoscience and Remote Sensing Symposium (InGARSS), 2023Sankalp Dhondi +4 more
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Dependent Component Analysis: A Hyperspectral Unmixing Algorithm
2007Linear unmixing decomposes a hyperspectral image into a collection of reflectance spectra of the materials present in the scene, called endmember signatures, and the corresponding abundance fractions at each pixel in a spatial area of interest. This paper introduces a new unmixing method, called Dependent Component Analysis (DECA), which overcomes ...
Nascimento, Jose, Bioucas-Dias, José M.
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Superpixel based unmixing for enhanced hyperspectral denoising
2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2016Unmixing based denoising for hyperspectral images is a recent addition to the literature, and aims to reconstruct the data using noise-free and pure spectral signatures and their abundances. Unmixing based denoising has the potential of providing enhanced denoising performance by excluding the noise effects in the endmember and abundance matrices, and ...
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Hyperspectral unmixing-based anomaly detection
Computational Imaging VII, 2023Mohammed S. H. Younis +4 more
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Regularization in Hyperspectral Unmixing
2017Jignesh S. Bhatt, Manjunath V. Joshi
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