Yaoming Cai, Zijia Zhang, Pedram Ghamisi, Yao Ding, Xiaobo Liu, Zhihua Cai, and Richard Gloaguen

Deep subspace clustering has achieved remarkable performances in the unsupervised classification of hyperspectral images. However, previous models based on pixel-level self-expressiveness of data suffer from the exponential growth of computational complexity and access memory requirements with increasing number of samples, thus leading to poor applicability to large hyperspectral images. This paper presents a Neighborhood Contrastive Subspace Clustering network (NCSC), a scalable and robust deep subspace clustering approach, for unsupervised classification of large hyperspectral images. Instead of using a conventional autoencoder, we devise a novel superpixel pooling autoencoder to learn the superpixel-level latent representation and subspace, allowing a contracted self-expressive layer. To encourage a robust subspace representation, we propose a novel neighborhood contrastive regularization to maximize the agreement between positive samples in subspace. We jointly train the resulting model in an end-to-end fashion by optimizing an adaptively weighted multi-task loss. Extensive experiments on three hyperspectral benchmarks demonstrate the effectiveness of the proposed approach and its substantial advancement of state-of-the-art approaches

IEEE Transactions on Geoscience and Remote Sensing, 2022-06-01.

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IARAI Authors
Dr. Pedram Ghamisi
Algorithms, Remote Sensing
Contrastive Learning, Hyperspectral Image, Remote Sensing, Subspace Clustering, Superpixel


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