Hyperspectral Unmixing Using Higher-Order Graph Regularized NMF With Adaptive Feature Selection

Author:

Qu Kewen1ORCID,Li Zhenqing1ORCID,Wang Chenyang1ORCID,Luo Fangzhou1ORCID,Bao Wenxing1ORCID

Affiliation:

1. Image and Intelligence Information Processing Innovation Team, National Ethnic Affairs Commission, Yinchuan, China

Funder

Natural Science Foundation of Ningxia Province of China

Science Foundation of North Minzu University, Yingchuan, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Graph feature fusion driven by deep autoencoder for advanced hyperspectral image unmixing;Knowledge-Based Systems;2024-09

2. Spatial–Spectral Hypergraph-Based Unsupervised Band Selection for Hyperspectral Remote Sensing Images;IEEE Sensors Journal;2024-09-01

3. Multiscale NMF based on intra-pixel and inter-pixel structure adjustment for spectral unmixing;International Journal of Applied Earth Observation and Geoinformation;2024-06

4. Hyperspectral Unmixing Using Reweighted Unidirectional TV Low-Rank NTF With Multiple-Factor Collaboration Regularization;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

5. A Fast Sparse NMF Optimization Algorithm for Hyperspectral Unmixing;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

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