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A greedy algorithm for sparse unmixing
2018 26th Signal Processing and Communications Applications Conference (SIU), 2018Hyperspectral 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 Gurkan Toker, Seniha Esen Yuksel
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Reweighted Sparse Regression for Hyperspectral Unmixing
IEEE Transactions on Geoscience and Remote Sensing, 2016Hyperspectral unmixing (HSU) plays an important role in hyperspectral image (HSI) analysis. Recently, the HSU method based on sparse regression has drawn much attention. This paper presents a new weighted sparse regression problem for HSU and proposes two iterative reweighted algorithms for solving this problem, where the weights used for the next ...
Cheng Yong Zheng +3 more
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Robust sparse unmixing of hyperspectral data
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016Sparse unmixing (SU) of hyperspectral data has recently received particular attention for analyzing remote sensing images, which aims at finding the optimal subset of signatures to best model the mixed pixel in the scene. However, most SU methods are based on the commonly admitted linear mixing model (LMM), which ignores the possible nonlinear effects (
Yong Ma, Chang Li, Jiayi Ma
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Sparse Dictionary Learning for Blind Hyperspectral Unmixing
IEEE Geoscience and Remote Sensing Letters, 2019Dictionary learning (DL) has been successfully applied to blind hyperspectral unmixing due to the similarity of underlying mathematical models. Both of them are linear mixture models and quite often sparsity and nonnegativity are incorporated. However, the mainstream sparse DL algorithms are crippled by the difficulty in prespecifying suitable sparsity.
Yang Liu +4 more
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Parallel method for sparse semisupervised hyperspectral unmixing
SPIE Proceedings, 2013Parallel hyperspectral unmixing problem is considered in this paper. A semisupervised approach is developed under the linear mixture model, where the abundance’s physical constraints are taken into account. The proposed approach relies on the increasing availability of spectral libraries of materials measured on the ground instead of resorting to ...
Nascimento, Jose +4 more
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Nonlocal similarity regularization for sparse hyperspectral unmixing
2014 IEEE Geoscience and Remote Sensing Symposium, 2014This paper is concerned with semisupervised hyperspectral unmixing using a nonlocal similarity prior on the abundance images. To this end, the nonlocal self-similarity regularization is incorporated into the classical sparse regression formula to propose a new model for hyperspectral sparse unmixing.
null Rui Wang, null Heng-Chao Li
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Deblurring and Sparse Unmixing for Hyperspectral Images
IEEE Transactions on Geoscience and Remote Sensing, 2013The 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 +4 more
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Local abundance regularization for hyperspectral sparse unmixing
2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2016Hyperspectral sparse unmixing is a task to estimate the optimal fraction (abundance) of materials contained in mixed pixels (endmembers) of a hyperspectral scene, by considering the abundance sparsity. The abundance has a unique property, i.e., high spatial correlation in local regions. This is due to the fact that the endmembers existing in the region
Mia Rizkinia, Masahiro Okuda
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Simultaneous sparse recovery for unsupervised hyperspectral unmixing
SPIE Proceedings, 2011Spectral pixels in a hyperspectral image are known to lie in a low-dimensional subspace. The Linear Mixture Model states that every spectral vector is closely represented by a linear combination of some signatures. When no prior knowledge of the representing signatures available, they must be extracted from the image data, then the abundances of ...
Dzung T. Nguyen +3 more
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Sparse hyperspectral unmixing with spatial discontinuity preservation
2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2016Sparse unmixing and sparse representation are known to be effective for improving the interpretation of remotely sensed hyperspectral images. Classic methods for incorporating spatial information into spectral unmixing assume that the abundances of the pixels are smooth and fall into a homogeneous region shared by the same endmembers and their ...
Shaoquan Zhang +3 more
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