Hyperspectral sparse fusion using adaptive total variation regularization and superpixel-based weighted nuclear norm
-
Published:2024-07
Issue:
Volume:220
Page:109449
-
ISSN:0165-1684
-
Container-title:Signal Processing
-
language:en
-
Short-container-title:Signal Processing
Author:
Lu Jingjing,
Zhang JunORCID,
Wang ChaoORCID,
Deng Chengzhi
Reference58 articles.
1. Graph-regularized fast and robust principal component analysis for hyperspectral band selection;Sun;IEEE Trans. Geosci. Remote Sens.,2018
2. Self-paced joint sparse representation for the classification of hyperspectral images;Peng;IEEE Trans. Geosci. Remote Sens.,2019
3. Object detection in optical remote sensing images: A survey and a new benchmark;Li;ISPRS J. Photogramm. Remote Sens.,2020
4. Spatial and spectral image fusion using sparse matrix factorization;Huang;IEEE Trans. Geosci. Remote Sens.,2014
5. R. Kawakami, Y. Matsushita, J. Wright, M. Ben-Ezra, Y.W. Tai, K. Ikeuchi, High-resolution hyperspectral imaging via matrix factorization, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2011, pp. 2329–2336.