Spatial Reconstruction Based on Spectral Metric for Hyperspectral Image Classification

Jie Fang, Yulu Zhong, Xiaoqian Cao, Dianwei Wang

Abstract: We present a hyperspectral image (HSI) classification method based on spatial reconstruction to alleviate the influences of view changes to HSI encoding, and it mainly contains a spatial reconstruction mechanism, a feature representation network (FRN), and an auxiliary branch. The spatial reconstruction mechanism based on spectral metric unifies image patches with the same entities and different neighbor distributions to an identical cube, while the FRN based on soft band selection adaptively emphasizes informative spectral bands and suppresses redundant ones in the coding phase, and these two modules can form spatial distribution-insensitive data space and noise-robust discriminative feature vector and further improve the classification performance. Besides, the auxiliary branch based on the decoupling strategy ensures the latent relationships among neighbor pixels of the original patch, and it also highlights the relative importance of the center pixel. In addition, the experimental results on three public datasets demonstrate the superiority of the proposed method.