Deep Spatial-Spectral Subspace Clustering for Hyperspectral Image

• Published in: IEEE transactions on circuits and systems for video technology Vol. 31; no. 7; pp. 2686 - 2697
• Main Authors: Lei, Jianjun, Li, Xinyu, Peng, Bo, Fang, Leyuan, Ling, Nam, Huang, Qingming
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Affinity; Algorithms; Clustering; Clustering algorithms; Clustering methods; Coders; Collaboration; Constraints; Data mining; deep learning; deep subspace clustering; Feature extraction; Hyperspectral image clustering; Hyperspectral imaging; Kernel; multi-scale auto-encoder; self-expressiveness similarity constraint; Spectra; Subspaces; Task analysis

Hyperspectral image (HSI) clustering is a challenging task due to the complex characteristics in HSI data, such as spatial-spectral structure, high-dimension, and large spectral variability. In this paper, we propose a novel deep spatial-spectral subspace clustering network (DS3 C -Net), which explo...