SquconvNet: Deep Sequencer Convolutional Network for Hyperspectral Image Classification

The application of Transformer in computer vision has had the most significant influence of all the deep learning developments over the past five years. In addition to the exceptional performance of convolutional neural networks (CNN) in hyperspectral image (HSI) classification, Transformer has begun to be applied to HSI classification. However, for the time being, Transformer has not produced satisfactory results in HSI classification. Recently, in the field of image classification, the creators of Sequencer have proposed a Sequencer structure that substitutes the Transformer self-attention layer with a BiLSTM2D layer and achieves satisfactory results. As a result, this paper proposes a unique network called SquconvNet, that combines CNN with Sequencer block to improve hyperspectral classification. In this paper, we conducted rigorous HSI classification experiments on three relevant baseline datasets to evaluate the performance of the proposed method. The experimental results show that our proposed method has clear advantages in terms of classification accuracy and stability.

Automated summary

The application of Transformer in computer vision has had a significant impact in the field of deep learning. While convolutional neural networks (CNN) have shown exceptional performance in hyperspectral image (HSI) classification, Transformer has not produced satisfactory results in that area. Recently, a Sequencer structure that replaces the Transformer self-attention layer with a BiLSTM2D layer has achieved satisfactory results in image classification. This paper proposes a unique network called SquconvNet, which combines CNN with Sequencer block to improve hyperspectral classification. Rigorous experiments on three relevant baseline datasets demonstrate that our proposed method outperforms in terms of classification accuracy and stability.