Deep Spatial-Spectral Subspace Clustering for Hyperspectral Images Based on Contrastive Learning

Remote Sensing

Teng Li¹  Yuanxi Peng²  Xiang Hu²  Tong Zhou² 

[1] Beijing Institute for Advanced Study, National University of Defense Technology, Beijing 100020, China;The State Key Laboratory of High-Performance Computing, College of Computer, National University of Defense Technology, Changsha 410073, China;

关键词: hyperspectral image clustering;    deep subspace clustering;    deep learning;    spectral clustering;   

DOI  :  10.3390/rs13214418

来源: DOAJ

【 摘 要 】

Hyperspectral image (HSI) clustering is a major challenge due to the redundant spectral information in HSIs. In this paper, we propose a novel deep subspace clustering method that extracts spatial–spectral features via contrastive learning. First, we construct positive and negative sample pairs through data augmentation. Then, the data pairs are projected into feature space using a CNN model. Contrastive learning is conducted by minimizing the distances of positive pairs and maximizing those of negative pairs. Finally, based on their features, spectral clustering is employed to obtain the final result. Experimental results gained over three HSI datasets demonstrate that our proposed method is superior to other state-of-the-art methods.

【 授权许可】

Unknown