Deep Diversity-Enhanced Feature Representation of Hyperspectral Images

arXiv:2301.06132v3 Announce Type: replace
Abstract: 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 …

arxiv cs.cv diversity eess.iv feature images representation type