Results 211 to 220 of about 423,442 (225)

Complete dictionary online learning for sparse unmixing

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016
Sparse unmixing has been successfully applied to hyperspectral remote sensing imagery, based on an available standard spectral library. However, as the number of hyperspectral remote sensors increases, more and more hyperspectral remote sensing images are requiring analysis without the use of a corresponding standard spectral library.
Ruyi Feng, Yanfei Zhong, Liangpei Zhang
openaire   +1 more source

Double-Weighted Spatial Low-Rank and Superpixel-Guided Adaptive Graph Laplacian Regularization for Sparse Hyperspectral Unmixing

IEEE Transactions on Instrumentation and Measurement
Sparse unmixing (SU) performs unmixing tasks for hyperspectral images (HSIs) using a complete spectral library. Recent studies have reported that the adverse effects of noise can be effectively mitigated by utilizing the spatial context of HSIs. However,
Taowei Wang, Weitao Chen
semanticscholar   +1 more source

Superpixel-guided manifold sparse nonnegative matrix factorization for hyperspectral unmixing

Journal of Applied Remote Sensing
. Hyperspectral unmixing (HU) is a critical technique for analyzing remote sensing images. Its primary goal is to extract endmembers and their corresponding abundances from complex hyperspectral images.
Denggang Li, Xinwan Zhang, Ying Zhang, Lianhui Liang, Xiangxiang Chen, Lin Jia   +5 more
semanticscholar   +1 more source

Branch-and-Bound Algorithm for Exact $\ell_{0}$-Norm Sparse Spectral Unmixing

European Signal Processing Conference
We propose an algorithm that exactly solves the cardinality-constrained sparse spectral unmixing problem. Based on recent works on $\ell_{0}$-norm exact optimization, a branch-and-bound architecture is specifically developed for sparse unmixing, under ...
Mehdi Latif, Gwenaël Samain, Nils Foix-Colonier, Sébastien Bourguignon   +3 more
semanticscholar   +1 more source

A subspace-based total variation regularization for sparse hyperspectral image unmixing

International Journal of Remote Sensing
The objective of hyperspectral image unmixing is to determine the fractional abundances of constituent materials within mixed pixels. With large spectral libraries available, sparse spectral unmixing assumes that each mixed pixel consists of only a few ...
Yu-Bin Cai, Jie Huang
semanticscholar   +1 more source

Kernel sparse NMF for hyperspectral unmixing

2014 International Conference on Orange Technologies, 2014
Bei Fang, Ying Li, Peng Zhang, Bendu Bai
openaire   +1 more source

A non-local sparse unmixing based hyperspectral change detection with unsupervised deep clustering

Knowledge-Based Systems
Tianqi Gao, Maoguo Gong, Xiangming Jiang, Yue Zhao, Hao Liu, Yan Pu   +5 more
semanticscholar   +1 more source

Robust spatially regularized sparse unmixing of hyperspectral remote sensing images with spectral library pruning

Infrared Physics & Technology
Shaoquan Zhang, Yuyang Liu, Fan Li, Jiajun Zheng, Pengfei Lai, Chengzhi Deng, Mengxiong Tang, Shengqian Wang   +7 more
semanticscholar   +1 more source

Sparse Unmixing Guided Adversarial Attack for Hyperspectral Image Classification

IEEE transactions on circuits and systems for video technology (Print)
Hao Li, Kelin Dang, Maoguo Gong, A. K. Qin, Yu Zhou, Yue Wu, Lining Xing   +6 more
semanticscholar   +1 more source

Evolutionary multitasking cooperative transfer for multiobjective hyperspectral sparse unmixing

Knowledge-Based Systems, 2023
Jianzhao Li, Maoguo Gong, Jinxin Wei, Yourun Zhang, Yue Zhao, Shanfeng Wang, Xiangming Jiang   +6 more
semanticscholar   +1 more source