Segmented Recurrent Transformer With Cubed 3-D-Multiscanning Strategy for Hyperspectral Image Classification

This study introduces an innovative approach to hyperspectral imaging (HSI) classification by integrating convolution, recurrence, and self-attention mechanisms in a 3-D configuration. We address several challenges such as the 1) disruption of spectral continuity by traditional dimensionality reduction methods like PCA, 2) the overlooking of band-to-band continuous features in existing spatial-only 2-D multiscanning strategy, and 3) the limitations in model design by simply cascading recurrent neural networks (RNNs) with transformers for HSI analysis. Our solution involves three core components: 1) subband grouping with group-wise convolution for refined dimension reduction; 2) a novel cubed 3-D-multiscanning technique enabling thorough multidirectional analysis in both spectral and spatial domains; and 3) the development of a Cubic-Net framework with a specially designed segmented recurrent transformer (SRT). This SRT is tailored to effectively utilize spectral continuity along with spatial contextual features, overcoming common sequential data analysis challenges seen in RNNs and transformers. Furthermore, our feature fusion strategy successively integrates “short-term” and “long-term” SRT features, thereby enhancing the model’s ability to process both spectral and spatial features effectively. Experimental results from three public HSI datasets indicate our method’s improved performance over existing baselines and state-of-the-art methods. This research offers a new perspective on 3-D sequential HSI classification.