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A Variable-Iterative Fully Convolutional Neural Network for Sparse Unmixing
Photogrammetric Engineering & Remote SensingNeural networks have greatly promoted the development of hyperspectral unmixing (HU). Most data-driven deep networks extract features of hyperspectral images (HSIs) by stacking convolutional layers to achieve endmember extraction and abundance estimation.
Fanqiang Kong +5 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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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 +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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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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Dual Spatial Weighted Sparse Hyperspectral Unmixing
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 2022Yonggang Chen +5 more
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