Fast and Latent Low-Rank Subspace Clustering for Hyperspectral Band Selection

• Published in: IEEE transactions on geoscience and remote sensing Vol. 58; no. 6; pp. 3906 - 3915
• Main Authors: Sun, Weiwei, Peng, Jiangtao, Yang, Gang, Du, Qian
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Affinity; Algorithms; Band selection; Banded structure; Centroids; Clustering; Clustering algorithms; Coefficients; Computational efficiency; Computer applications; Computing costs; correntropy measure; Dictionaries; hyperspectral imagery (HSI); Hyperspectral imaging; Lagrange multiplier; latent low-rank subspace clustering; Optimization; remote sensing; Representations; Similarity; Sparse matrices; Subspace methods; Subspaces

This article presents a fast and latent low-rank subspace clustering (FLLRSC) method to select hyperspectral bands. The FLLRSC assumes that all the bands are sampled from a union of latent low-rank independent subspaces and formulates the self-representation property of all bands into a latent low-r...