Self-Supervised Deep Subspace Clustering for Hyperspectral Images With Adaptive Self-Expressive Coefficient Matrix Initialization
Kun Li, Yao Qin, Qiang Ling, Yingqian Wang, Zaiping Lin, Wei An
Abstract
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.