Investigation of Hierarchical Spectral Vision Transformer Architecture for Classification of Hyperspectral Imagery

Abstract

In the past three years, there has been significant interest in hyperspectral imagery (HSI) classification using vision Transformers for the analysis of remotely sensed data. Previous research predominantly focused on the empirical integration of convolutional neural networks (CNNs) to augment the network’s capability to extract local feature information. Yet, the theoretical justification for vision Transformers out-performing CNN architectures in HSI classification remains a question. To address this issue, a unified hierarchical spectral vision Transformer architecture, specifically tailored for HSI classification, is investigated. In this streamlined yet effective vision Transformer architecture, multiple mixer modules are strategically integrated separately. These include the CNN mixer, which executes convolutional operations; the spatial self-attention (SSA) mixer and channel self-attention (CSA) mixer, both of which are adaptations of classical self-attention blocks; and hybrid models, such as the SSA + CNN mixer and CSA + CNN mixer, which merge convolution with self-attention operations. This integration facilitates the development of a broad spectrum of vision Transformer-based models tailored for HSI classification. In terms of the training process, a comprehensive analysis is performed, contrasting classical CNN models and vision Transformer-based counterparts, with particular attention to disturbance robustness and the distribution of the largest eigenvalue of the Hessian. From the evaluations conducted on various mixer models rooted in the unified architecture, it is concluded that the unique strength of vision Transformers can be attributed to their overarching architecture, rather than being exclusively reliant on individual multihead self-attention (MSA) components. Extensive experiments demonstrate that the derived vision Transformer models, based on the unified architecture, surpass the classical methods when applied to multiple hyperspectral benchmark datasets.