Nested Transformers for Hyperspectral Image Classification

Abstract

Substantial deep learning methods have been utilized for hyperspectral image (HSI) classification recently. Vision Transformer (ViT) is skilled in modeling the overall structure of images and has been introduced to HSI classification task. However, the fixed patch division operation in ViT may lead to insufficient feature extraction, especially the features of the edges between patches will be ignored. To address this problem, we devise a workflow for HSI classification based on the Nested Transformers (NesT). The NesT employs the block aggregation module to extract edge information between patches, which realizes cross-block communication of nonlocal information and optimizes global information extraction. In this paper, the NesT is used for HSI classification for the first time. The experiments are carried out on four widely used hyperspectral datasets: Indian Pines, Salinas, Tea Farm, and Xiongan New Area (Matiwan Village). The obtained results reveal that the NesT can provide competitive results compared to conventional machine learning and deep learning methods and achieve top accuracy on four datasets, which proves the superiority of the NesT in HSI classification with limited training samples.