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

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 global dictionary atoms. In this article, we propose an end-to-end trainable HSI Specifically, ensure extracted features well-suited subsequent clustering, cluster assignments with confidence employed as pseudo-labels supervise learning process. Then, adaptive self-expressive coefficient matrix initialization strategy is designed reduce redundancy, where spectral similarity between each target sample neighbors modeled via k-nearest neighbor graph guide initialization. Experimental results on three public datasets demonstrate proposed method. particular, our method outperforms several state-of-the-art methods, achieves overall accuracy 100% both SalinasA Pavia University datasets.