SPNet: A Spectral Patching Network for End-To-End Hyperspectral Image Classification

Deep learning (DL)-based hyperspectral classification primarily use "spatial patching" as preprocessing for incorporating local spatial information. This operation can help to promote classification accuracy but faces the following problems. First, it is difficult to determine the optimal size of spatial patches for different hyperspectral images (HSIs). Second, this operation only exploits spatial features locally but not globally. In this paper, we propose a novel spectral patching network (SPNet) with an end-to-end deep learning architecture for HSI classification. SPNet uses "spectral patching" and Atrous Spatial Pyramid Pooling (ASPP) module to fully preserve the local and global spatial contextual information of original HSIs. The experimental results with UAV-borne hyperspectral dataset demonstrate that the SPNet achieved state-of-the-art accuracy and visualization performance in.