Channel-Reduced Transformer With Cross-Region Tokenization for Hyperspectral Image Classification

Transformers have been widely adopted in the field of hyperspectral image (HSI) classification. However, a significant drawback of transformers lies in their excessive number of parameters and the high computational overhead. To address this challenge, we propose a Channel-Reduced Transformer (CRFormer) for HSI classification. In an effort to enhance computational efficiency, we first introduce a cross-region tokenization (CRT) approach. This method effectively shortens the sequence length input to the transformer, thereby alleviating the computational burden. Additionally, we propose a channel-reduced multi-head self-attention (CR-MHSA) module. This module operates on only half of the input channels while still attaining comparable or even superior results. Experimental results conducted on three benchmark datasets demonstrate that our proposed method not only achieves superior classification accuracy but also significantly reduces computational complexity compared to other transformer-based approaches.