The high dimensionality of hyperspectral images often results in the degradation of clustering performance. Due to the powerful ability of deep feature extraction and non-linear feature representation, the clustering algorithm based on deep learning has become a hot research topic in the field of hyperspectral remote sensing. However, most deep clustering algorithms for hyperspectral images utilize deep neural networks as feature extractor without considering prior knowledge constraints that are suitable for clustering. To solve this problem, we propose an intra-class distance constrained deep clustering algorithm for high-dimensional hyperspectral images. The proposed algorithm constrains the feature mapping procedure of the auto-encoder network by intra-class distance so that raw images are transformed from the original high-dimensional space to the low-dimensional feature space that is more conducive to clustering. Furthermore, the related learning process is treated as a joint optimization problem of deep feature extraction and clustering. Experimental results demonstrate the intense competitiveness of the proposed algorithm in comparison with state-of-the-art clustering methods of hyperspectral images.

中文翻译：

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高光谱图像类内距离约束的深度聚类

高光谱图像的高维通常会导致聚类性能的下降。由于深度特征提取和非线性特征表示的强大能力，基于深度学习的聚类算法已成为高光谱遥感领域的研究热点。然而，大多数用于高光谱图像的深度聚类算法利用深度神经网络作为特征提取器，而没有考虑适合聚类的先验知识约束。为了解决这个问题，我们提出了一种用于高维高光谱图像的类内距离约束深度聚类算法。所提出的算法通过类内距离约束自编码器网络的特征映射过程，使原始图像从原始高维空间转换到更有利于聚类的低维特征空间。此外，相关的学习过程被视为深度特征提取和聚类的联合优化问题。实验结果表明，与最先进的高光谱图像聚类方法相比，所提出的算法具有很强的竞争力。