Self-Supervised Deep Subspace Clustering for Hyperspectral Images With Adaptive Self-Expressive Coefficient Matrix Initialization

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing 2021, Vol.14, p.3215-3227
• Main Authors: Li, Kun, Qin, Yao, Ling, Qiang, Wang, Yingqian, Lin, Zaiping, An, Wei
• Format: Article
• Language: English
• Subjects: Clustering; Clustering methods; Datasets; Deep subspace clustering (DSC); Dictionaries; Feature extraction; Glossaries; hyperspectral image (HSI); Hyperspectral imaging; Learning; Redundancy; self-expressive; self-supervised; Sparse matrices; subspace clustering (SC); Subspaces; Task analysis
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2021.3063335

Deep subspace clustering network has shown its effectiveness in hyperspectral image (HSI) clustering. However, there are two major challenges that need to be addressed: 1) lack of effective supervision for feature learning; and 2) negative effect caused by the high redundancy of the global dictionary atoms. In this article, we propose an end-to-end trainable network for HSI clustering. Specifically, to ensure the extracted features are well-suited to subsequent subspace clustering, the cluster assignments with high confidence are employed as pseudo-labels to supervise the feature learning process. Then, an adaptive self-expressive coefficient matrix initialization strategy is designed to reduce the dictionary redundancy, where the spectral similarity between each target sample and its neighbors is modeled via the {k}-nearest neighbor graph to guide the initialization. Experimental results on three public HSI datasets demonstrate the effectiveness of the proposed method. In particular, our method outperforms several state-of-the-art HSI clustering methods, and achieves overall accuracy of 100% on both SalinasA and Pavia University datasets.