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 Gift Card 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 Feminine Medicated Products 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.