SpectralSWIN: a spectral-swin transformer network for hyperspectral image classification

• Published in: International journal of remote sensing Vol. 43; no. 11; pp. 4025 - 4044
• Main Authors: Ayas, Selen, Tunc-Gormus, Esra
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 03-06-2022; Taylor & Francis Ltd
• Subjects: Classification; Deep learning; Feature extraction; Hyperspectral imaging; Image classification; Machine learning; Transformers
• ISSN: 0143-1161; 1366-5901
• DOI: 10.1080/01431161.2022.2105668

Hyperspectral image (HSI) classification has received extensive attention by the development of deep learning and has achieved great success. However, most of the deep learning-based approaches tend to extract features of spatial content by disrupting spectral information or to extract sequential spectral features in short-range context. On the other hand, Transformers-based models address this problem by learning long-range relationship. This study introduces a novel spectral-swin transformer (SpectralSWIN) network. The proposed network effectively projects the HSI data from spectral characteristics into spatial and spectral feature representation. Specifically, SpectralSWIN network makes use of a newly proposed swin-spectral module (SSM) for processing the spatial and spectral features concurrently. As far as we know, this is the first time that a transformer backbone designed for vision domain has been proposed for HSI classification. Extensive experiments conducted on two different HSI prove the superiority and effectiveness of the proposed method over the state-of-the-art methods in terms of both quantitative and visual evaluations.