Deep Diversity-Enhanced Feature Representation of Hyperspectral Images
This work addresses efficient feature representation for hyperspectral image processing, which is incremental as it builds on existing convolution methods with novel modifications.
The paper tackled the problem of embedding high-dimensional spatio-spectral information in hyperspectral images by enhancing feature diversity, resulting in a method that outperforms state-of-the-art approaches in tasks like denoising, super-resolution, and classification with significant quantitative and qualitative improvements.
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.