Results 11 to 20 of about 50 (50)

Manifold regularization for sparse unmixing of hyperspectral images [PDF]

SpringerPlus, 2016
Recently, sparse unmixing has been successfully applied to spectral mixture analysis of remotely sensed hyperspectral images. Based on the assumption that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance, unmixing of each mixed pixel in the scene is to find an ...
Liu, Junmin, Zhang, Chunxia, Zhang, Jiangshe, Li, Huirong, Gao, Yuelin   +4 more
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Sparse Unmixing using an approximate L0 Regularization [PDF]

Advances in Intelligent Systems Research, 2015
Recently, sparse unmixing focuses on finding an optimal subset of spectral signatures in a large spectral spetral library. In most previous work concerned with the sparse unmixing, the linear mixture model has been widely used to determine and quantify the abundance of materials in mixed piexels(1). In this paper, we propose a new sparse unmxing method
JianPing Xiao, Shengqian Wang, Chengzhi Deng, Tai Gao, Yang Guo   +4 more
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Hyperspectral change detection by sparse unmixing with dictionary pruning

2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2015
Hyperspectral change detection is used in many applications ranging from environmental monitoring to city planning and military surveillance. Change detection by unmixing has the potential of not only providing the locations of the changes, but also the nature of the change, and sub-pixel level information.
Ertiirk, Alp, Iordache, Marian-Daniel, Plaza, Antonio   +2 more
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NMF-based sparse unmixing of complex mixtures

, 2019
In this work, we are interested in unmixing complex mixtures based on Nuclear Magnetic Resonance spectroscopy spectra. More precisely, we propose to solve a 2D blind source separation problem where signals (spectra) are highly sparse. The separation is formulated as a nonnegative matrix factorization problem that is solved using a block coordinate ...
Cherni, Afef, Piersanti, Elena, Chaux, Caroline   +2 more
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Fast Hyperspectral Unmixing via Reweighted Sparse Regression

IEICE Transactions on Information and Systems, 2019
Hongwei HAN, Ke GUO, Maozhi WANG, Tingbin ZHANG, Shuang ZHANG   +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, Ting-Zhu Huang, Xi-Le Zhao, Liang-Jian Deng   +3 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, Jose M. Bioucas-Dias   +3 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, Jose Bioucas-Dias, Antonio Plaza   +2 more
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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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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, Jose M. Bioucas-Dias   +3 more
openaire   +1 more source