Deep subspace clustering (DSC) 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 an increasing number of samples, thus leading to poor applicability to large hyperspectral images. This article presents a neighborhood contrastive subspace clustering (NCSC) network, a scalable and robust DSC 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 multitask loss. Extensive experiments on three hyperspectral benchmarks demonstrate the effectiveness of the proposed approach and its substantial advancement of state-of-the-art approaches.

中文翻译：

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用于高光谱图像的超像素收缩邻域对比子空间聚类网络

深子空间聚类（DSC）在高光谱图像的无监督分类中取得了显着的表现。然而，以往基于像素级数据自我表达的模型随着样本数量的增加，计算复杂度和访问内存需求呈指数增长，从而导致对大型高光谱图像的适用性较差。本文介绍了一种邻域对比子空间聚类 (NCSC) 网络，这是一种可扩展且稳健的 DSC 方法，用于对大型高光谱图像进行无监督分类。我们没有使用传统的自动编码器，而是设计了一种新颖的超像素池自动编码器来学习超像素级潜在表示和子空间，从而允许收缩的自我表达层。为了鼓励稳健的子空间表示，我们提出了一种新颖的邻域对比正则化来最大化子空间中正样本之间的一致性。我们通过优化自适应加权多任务损失，以端到端的方式联合训练生成的模型。对三个高光谱基准的广泛实验证明了所提出方法的有效性及其对最先进方法的实质性进步。