Sparsity-Based Clustering for Large Hyperspectral Remote Sensing Images

Hyperspectral image (HSI) clustering is extremely challenging because of the complexity of the image structure. Recently, the subspace clustering algorithms have achieved competitive performance for HSIs. However, these methods generally are computationally complex and time-and-memory-consuming, giv...

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Bibliographic Details
Published in:	IEEE transactions on geoscience and remote sensing Vol. 59; no. 12; pp. 10410 - 10424
Main Authors:	Zhai, Han, Zhang, Hongyan, Zhang, Liangpei, Li, Pingxiang
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.12.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Biological system modeling Clustering Clustering algorithms Clustering methods Complexity Computational modeling Computer applications Dictionaries Encoding hyperspectral image (HSI) Hyperspectral imaging Image segmentation joint sparse coding Optimization Pixels Recovery recovery residual Remote sensing sparse coding Sparsity
ISSN:	0196-2892, 1558-0644
Online Access:	Get full text
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