SpectralDiff: A Generative Framework for Hyperspectral Image Classification with Diffusion Models

• URL: http://arxiv.org/abs/2304.05961v2
• Date: Fri, 1 Sep 2023 04:09:37 GMT
• Title: SpectralDiff: A Generative Framework for Hyperspectral Image Classification with Diffusion Models
• Authors: Ning Chen, Jun Yue, Leyuan Fang, Shaobo Xia
• Abstract summary: We propose a generative framework for HSI classification with diffusion models (SpectralDiff) SpectralDiff effectively mines the distribution information of high-dimensional and highly redundant data. Experiments on three public HSI datasets demonstrate that the proposed method can achieve better performance than state-of-the-art methods.
• Score: 18.391049303136715
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hyperspectral Image (HSI) classification is an important issue in remote sensing field with extensive applications in earth science. In recent years, a large number of deep learning-based HSI classification methods have been proposed. However, existing methods have limited ability to handle high-dimensional, highly redundant, and complex data, making it challenging to capture the spectral-spatial distributions of data and relationships between samples. To address this issue, we propose a generative framework for HSI classification with diffusion models (SpectralDiff) that effectively mines the distribution information of high-dimensional and highly redundant data by iteratively denoising and explicitly constructing the data generation process, thus better reflecting the relationships between samples. The framework consists of a spectral-spatial diffusion module, and an attention-based classification module. The spectral-spatial diffusion module adopts forward and reverse spectral-spatial diffusion processes to achieve adaptive construction of sample relationships without requiring prior knowledge of graphical structure or neighborhood information. It captures spectral-spatial distribution and contextual information of objects in HSI and mines unsupervised spectral-spatial diffusion features within the reverse diffusion process. Finally, these features are fed into the attention-based classification module for per-pixel classification. The diffusion features can facilitate cross-sample perception via reconstruction distribution, leading to improved classification performance. Experiments on three public HSI datasets demonstrate that the proposed method can achieve better performance than state-of-the-art methods. For the sake of reproducibility, the source code of SpectralDiff will be publicly available at https://github.com/chenning0115/SpectralDiff.

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