Sigger, Neetu, Vien, Quoc-Tuan, Nguyen, Sinh Van, Tozzi, Gianluca and Nguyen, Tuan Thanh ORCID: 0000-0003-0055-8218 (2024) Unveiling the potential of diffusion model-based framework with transformer for hyperspectral image classification. Scientific Reports, 14:8438. ISSN 2045-2322 (Online) (doi:https://doi.org/10.1038/s41598-024-58125-4)

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Abstract

Hyperspectral imaging has gained popularity for analysing remotely sensed images in various fields such as agriculture and medical. However, existing models face challenges in dealing with the complex relationships and characteristics of spectral–spatial data due to the multi-band nature and data redundancy of hyperspectral data. To address this limitation, we propose a novel approach called DiffSpectralNet, which combines diffusion and transformer techniques. The diffusion method is able extract diverse and meaningful spectral–spatial features, leading to improvement in HSI classification. Our approach involves training an unsupervised learning framework based on the diffusion model to extract high-level and low-level spectral–spatial features, followed by the extraction of intermediate hierarchical features from different timestamps for classification using a pre-trained denoising U-Net. Finally, we employ a supervised transformer-based classifier to perform the HSI classification. We conduct comprehensive experiments on three publicly available datasets to validate our approach. The results demonstrate that our framework significantly outperforms existing approaches, achieving state-of-the-art performance. The stability and reliability of our approach are demonstrated across various classes in all datasets.

Item Type:	Article
Uncontrolled Keywords:	hyperspectral image classification, image analysis, diffusion technique, transformer technique
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Engineering (ENG)
Last Modified:	31 Jul 2024 13:12
URI:	http://gala.gre.ac.uk/id/eprint/47644

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