Unveiling the potential of diffusion model‑based framework with transformer for hyperspectral image classification
Article
Sigger, N., Vien, Q., Nguyen, S., Tozzi, G. and Nguyen, T. 2024. Unveiling the potential of diffusion model‑based framework with transformer for hyperspectral image classification. Scientific Reports. 14. https://doi.org/10.1038/s41598-024-58125-4
Type | Article |
---|---|
Title | Unveiling the potential of diffusion model‑based framework with transformer for hyperspectral image classification |
Authors | Sigger, N., Vien, Q., Nguyen, S., Tozzi, G. and Nguyen, T. |
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 to 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. |
Sustainable Development Goals | 11 Sustainable cities and communities |
Middlesex University Theme | Sustainability |
Research Group | London Digital Twin Research Centre |
Publisher | Nature Research |
Journal | Scientific Reports |
ISSN | |
Electronic | 2045-2322 |
Publication dates | |
10 Apr 2024 | |
Online | 10 Apr 2024 |
Publication process dates | |
Accepted | 26 Mar 2024 |
Accepted | 22 Dec 2023 |
Deposited | 24 May 2024 |
Output status | Published |
Publisher's version | License File Access Level Open |
Copyright Statement | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
Digital Object Identifier (DOI) | https://doi.org/10.1038/s41598-024-58125-4 |
Language | English |
https://repository.mdx.ac.uk/item/141420
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