Results 161 to 170 of about 423,442 (225)

Hyperspectral Sparse Unmixing via Nonconvex Shrinkage Penalties

IEEE Transactions on Geoscience and Remote Sensing, 2023
Hyperspectral sparse unmixing aims at finding the optimal subset of spectral signatures in the given spectral library and estimating their proportions in each pixel.
Longfei Ren, D. Hong, Lianru Gao, Xu Sun, Min Huang, J. Chanussot   +5 more
semanticscholar   +4 more sources

SUnCNN: Sparse Unmixing Using Unsupervised Convolutional Neural Network

IEEE Geoscience and Remote Sensing Letters, 2022
In this letter, we propose a sparse unmixing technique using a convolutional neural network (SUnCNN) for hyperspectral images. SUnCNN is the first deep learning-based technique proposed for sparse unmixing. It uses a deep convolutional encoder–decoder to
Behnood Rasti, Bikram Koirala
semanticscholar   +4 more sources

Multiobjective sparse unmixing based hyperspectral change detection

Applied Soft Computing
Xiangming Jiang, Tianqi Gao, Maoguo Gong, Fenlong Jiang, Mingyang Zhang, Jieyi Liu   +5 more
semanticscholar   +2 more sources

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
openaire   +1 more source

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
openaire   +1 more source

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
openaire   +1 more source

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
openaire   +1 more source

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