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Collaborative Sparse Regression for Hyperspectral Unmixing [PDF]

IEEE Transactions on Geoscience and Remote Sensing, 2014
Sparse unmixing has been recently introduced in hyperspectral imaging as a framework to characterize mixed pixels. It assumes that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance (e.g., spectra collected on the ground by a field spectroradiometer).
J Bioucas-Dias, Antonio Plaza, Marian-Daniel Iordache   +2 more
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Improving the performance of sparse unmixing

2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2013
Sparse unmixing has been proposed for hyperspectral image analysis. It has been shown that improved performance can be achieved when endmembers from a spectral library are used. However, when endmembers from image data have to be employed for unmixing, such a sparse-constrained approach may be problematic due to the fact that endmembers are generally ...
Qian Du 0001, Ben Ma, Nareenart Raksuntorn   +2 more
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Unmixing sparse hyperspectral mixtures

2009 IEEE International Geoscience and Remote Sensing Symposium, 2009
Finding an accurate sparse approximation of a spectral vector described by a linear model, when there is available a library of possible constituent signals (called endmembers or atoms), is a hard combinatorial problem which, as in other areas, has been increasingly addressed. This paper studies the efficiency of the sparse regression techniques in the
Marian-Daniel Iordache, José M. Bioucas-Dias, Antonio Plaza   +2 more
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Sparse distributed hyperspectral unmixing

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016
Blind hyperspectral unmixing is the task of jointly estimating the spectral signatures of material in a hyperspectral images and their abundances at each pixel. The size of hyperspectral images are usually very large, which may raise difficulties for classical optimization algorithms, due to limited memory of the hardware used.
Jakob Sigurdsson, Magnus O. Ulfarsson, Johannes R. Sveinsson, José M. Bioucas-Dias   +3 more
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A greedy algorithm for sparse unmixing

2018 26th Signal Processing and Communications Applications Conference (SIU), 2018
Hyperspectral imaging sensors provide image data containing both spatial and detailed spectral information. However, due to low spatial resolution, the pixels in hyperspectral images are actually mixtures of the spectral signatures of the materials. Sparse unmixing assumes that these mixed pixels are sparse linear combinations of different material ...
Kemal Gürkan Toker, Seniha Esen Yüksel
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Sparse unmixing with adaptive background

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017
We propose a new hyperspectral sparse unmixing method under the assumption of the availability of a spectral library. Hyperspectral signals inevitably possess non-linearity or distortion caused by the presence of endmembers outside of the collection, inaccurate measurement of atmosphere, and endmember mismatches.
Yuki Itoh, Mario Parente
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Robust Sparse Unmixing for Hyperspectral Imagery

IEEE Transactions on Geoscience and Remote Sensing, 2018
A linear sparse unmixing method based on spectral library has been widely used to tackle the hyperspectral unmixing problem, under the assumption that the spectrum of each pixel in the hyperspectral scene can be expressed as a linear combination of pure endmembers in the spectral library.
Dan Wang 0005, Zhenwei Shi 0001, Xinrui Cui   +2 more
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Deblurring and Sparse Unmixing for Hyperspectral Images

IEEE Transactions on Geoscience and Remote Sensing, 2013
The main aim of this paper is to study total variation (TV) regularization in deblurring and sparse unmixing of hyperspectral images. In the model, we also incorporate blurring operators for dealing with blurring effects, particularly blurring operators for hyperspectral imaging whose point spread functions are generally system dependent and formed ...
Xi-Le Zhao, Fan Wang, Ting-Zhu Huang, Michael K. Ng 0001, Robert J. Plemmons   +4 more
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Nonlocal Tensor-Based Sparse Hyperspectral Unmixing

IEEE Transactions on Geoscience and Remote Sensing, 2021
Sparse unmixing is an important technique for analyzing and processing hyperspectral images (HSIs). Simultaneously exploiting spatial correlation and sparsity improves substantially abundance estimation accuracy. In this article, we propose to exploit nonlocal spatial information in the HSI for the sparse unmixing problem.
Jie Huang 0005, Ting-Zhu Huang, Xi-Le Zhao, Liang-Jian Deng   +3 more
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Sparse Distributed Multitemporal Hyperspectral Unmixing

IEEE Transactions on Geoscience and Remote Sensing, 2017
Blind hyperspectral unmixing jointly estimates spectral signatures and abundances in hyperspectral ima-ges (HSIs). Hyperspectral unmixing is a powerful tool for analyzing hyperspectral data. However, the usual huge size of HSIs may raise difficulties for classical unmixing algorithms, namely, due to limitations of the hardware used.
Jakob Sigurdsson, Magnus O. Ulfarsson, Johannes R. Sveinsson, José M. Bioucas-Dias   +3 more
openaire   +1 more source