Geodesic
Adaptive Multiscale Sparse Unmixing for Hyperspectral Remote Sensing Image
Computer Science and Information Systems, Tome 20 (2023) no. 2 Cet article a éte moissonné depuis la source Computer Science and Information Systems website

Voir la notice de l'article

Sparse unmixing of hyperspectral images aims to separate the endmembers and estimate the abundances of mixed pixels. This approach is the essential step for many applications involving hyperspectral images. The multiscale spatial sparse hyperspectral unmixing algorithm (MUA) could achieve higher accuracy than many state-of-the-art algorithms. The regularization parameters, whose combinations markedly influence the unmixing accuracy, are determined by manually searching in the broad parameter space, leading to time consuming. To settle this issue, the adaptive multi-scale spatial sparse hyperspectral unmixing algorithm (AMUA) is proposed. Firstly, the MUA model is converted into a new version by using of a maximum a posteriori (MAP) system. Secondly, the theories indicating that andnorms are equivalent to Laplacian and multivariate Gaussian functions, respectively, are applied to explore the strong connections among the regularization parameters, estimated abundances and estimated noise variances. Finally, the connections are applied to update the regularization parameters adaptively in the optimization process of unmixing. Experimental results on both simulated data and real hyperspectral images show that the AMUA can substantially improve the unmixing efficiency at the cost of negligible accuracy. And a series of sensitive experiments were undertook to verify the robustness of the AMUA algorithm.
Keywords: adaptive multiscale sparse hyperspectral unmixing algorithm, loss functions, regularization parameters, maximum a posteriori. 
@article{CSIS_2023_20_2_a1,
     author = {Yalan Li and Yixuan Li and Wenwu Xie and Qian Du and Jing Yuan and Shang Lin and Liand Qi Chen},
     title = {Adaptive {Multiscale} {Sparse} {Unmixing} for {Hyperspectral} {Remote} {Sensing} {Image}},
     journal = {Computer Science and Information Systems},
     year = {2023},
     volume = {20},
     number = {2},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2023_20_2_a1/}
}
TY  - JOUR
AU  - Yalan Li
AU  - Yixuan Li
AU  - Wenwu Xie
AU  - Qian Du
AU  - Jing Yuan
AU  - Shang Lin
AU  - Liand Qi Chen
TI  - Adaptive Multiscale Sparse Unmixing for Hyperspectral Remote Sensing Image
JO  - Computer Science and Information Systems
PY  - 2023
VL  - 20
IS  - 2
UR  - http://geodesic.mathdoc.fr/item/CSIS_2023_20_2_a1/
ID  - CSIS_2023_20_2_a1
ER  - 
%0 Journal Article
%A Yalan Li
%A Yixuan Li
%A Wenwu Xie
%A Qian Du
%A Jing Yuan
%A Shang Lin
%A Liand Qi Chen
%T Adaptive Multiscale Sparse Unmixing for Hyperspectral Remote Sensing Image
%J Computer Science and Information Systems
%D 2023
%V 20
%N 2
%U http://geodesic.mathdoc.fr/item/CSIS_2023_20_2_a1/
%F CSIS_2023_20_2_a1
Yalan Li; Yixuan Li; Wenwu Xie; Qian Du; Jing Yuan; Shang Lin; Liand Qi Chen. Adaptive Multiscale Sparse Unmixing for Hyperspectral Remote Sensing Image. Computer Science and Information Systems, Tome 20 (2023) no. 2. http://geodesic.mathdoc.fr/item/CSIS_2023_20_2_a1/