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Manifold Regularized Sparse NMF for Hyperspectral Unmixing
IEEE Transactions on Geoscience and Remote Sensing, 2013Hyperspectral unmixing is one of the most important techniques in analyzing hyperspectral images, which decomposes a mixed pixel into a collection of constituent materials weighted by their proportions. Recently, many sparse nonnegative matrix factorization (NMF) algorithms have achieved advanced performance for hyperspectral unmixing because they ...
Xiaoqiang Lu +4 more
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On the performance of sparse unmixing on non-linear mixtures
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016This paper explores the performance of sparse unmixing (SU) on non-linear mixtures. We consider SU as an endmember selection method from a spectral library and measure its performance using recently proposed approximately perfect recovery condition for sparse unmixing, comparing with non-negative least squares (NNLS). We also further explore the effect
Yuki Itoh, Mario Parente
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Double reweighted sparse regression for hyperspectral unmixing
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016Spectral unmixing is an important technology in hyperspectral image applications. Recently, sparse regression is widely used in hyperspectral unmixing. This paper proposes a double reweighted sparse regression method for hyperspectral unmixing. The proposed method enhances the sparsity of abundance fraction in both spectral and spatial domains through ...
Rui Wang 0090 +3 more
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Multiobjective sparse unmixing approach with noise removal
Proceedings of the Genetic and Evolutionary Computation Conference, 2018In sparse hyperspectral unmixing, regularization methods inevitably suffer from the "decision ahead of solution" issue concerning the regularization parameter, which is not conducive to practical applications. To settle this issue, a two-phase multiobjective sparse unmixing (Tp-MoSU) approach has been proposed recently.
Xiangming Jiang +3 more
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Sparse unmixing based denoising for hyperspectral images
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016Until recently, hyperspectral image denoising was considered as a prior step to applications such as classification, detection, or unmixing. However, unmixing has been recently shown to also provide denoising due to its inherent property of representing pixels in terms of pure material signatures and their abundances. It is possible to eliminate sensor-
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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.
Rui Wang 0090, Heng-Chao Li 0001
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Total variation regulatization in sparse hyperspectral unmixing
2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2011Hyperspectral unmixing has recently been addressed as a sparse regression problem by using predefined spectral libraries instead of image-derived endmembers in the unmixing process. This new approach has attracted much attention, as it sidesteps well known obstacles met in endmember extraction, such as the stopping criteria for the extraction process ...
Marian-Daniel Iordache +2 more
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Framelet-Based Sparse Unmixing of Hyperspectral Images
IEEE Transactions on Image Processing, 2016Spectral unmixing aims at estimating the proportions (abundances) of pure spectrums (endmembers) in each mixed pixel of hyperspectral data. Recently, a semi-supervised approach, which takes the spectral library as prior knowledge, has been attracting much attention in unmixing.
Guixu Zhang, Yingying Xu, Faming Fang
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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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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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