In this paper, we study the problem of efficiently and effectively embedding the high-dimensional spatio-spectral information of hyperspectral (HS) images, guided by feature diversity. Specifically, based on the theoretical formulation that feature diversity is correlated with the rank of the unfolded kernel matrix, we rectify 3D convolution by modifying its topology to enhance the rank upper-bound. This modification yields a rank-enhanced spatial-spectral symmetrical convolution set (ReS$^3$-ConvSet), which not only learns diverse and powerful feature representations but also saves network parameters. Additionally, we also propose a novel diversity-aware regularization (DA-Reg) term that directly acts on the feature maps to maximize independence among elements. To demonstrate the superiority of the proposed ReS$^3$-ConvSet and DA-Reg, we apply them to various HS image processing and analysis tasks, including denoising, spatial super-resolution, and classification. Extensive experiments show that the proposed approaches outperform state-of-the-art methods both quantitatively and qualitatively to a significant extent. The code is publicly available at https://github.com/jinnh/ReSSS-ConvSet.

翻译：本文研究如何在高光谱图像中高效嵌入高维空间-光谱信息的问题，并以特征多样性为引导。具体而言，基于特征多样性与展开核矩阵秩相关的理论公式，我们通过修改3D卷积的拓扑结构来提升其秩的上界，从而得到秩增强的空间-光谱对称卷积集（ReS$^3$-ConvSet）。该卷积集不仅能学习多样且强大的特征表示，还能节省网络参数。此外，我们提出了一种新的多样性感知正则化项（DA-Reg），该正则项直接作用于特征图以最大化元素间的独立性。为证明所提出的ReS$^3$-ConvSet与DA-Reg的优越性，我们将它们应用于多种高光谱图像处理与分析任务，包括去噪、空间超分辨率和分类。大量实验表明，所提方法在定量和定性上均显著优于最先进方法。代码已公开在https://github.com/jinnh/ReSSS-ConvSet。