DiffUCD:Unsupervised Hyperspectral Image Change Detection with Semantic Correlation Diffusion Model

• URL: http://arxiv.org/abs/2305.12410v1
• Date: Sun, 21 May 2023 09:21:41 GMT
• Title: DiffUCD:Unsupervised Hyperspectral Image Change Detection with Semantic Correlation Diffusion Model
• Authors: Xiangrong Zhang, Shunli Tian, Guanchun Wang, Huiyu Zhou, and Licheng Jiao
• Abstract summary: Hyperspectral image change detection (HSI-CD) has emerged as a crucial research area in remote sensing. We propose a novel unsupervised HSI-CD with semantic correlation diffusion model (DiffUCD) Our method can achieve comparable results to those fully supervised methods requiring numerous samples.
• Score: 46.68717345017946
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hyperspectral image change detection (HSI-CD) has emerged as a crucial research area in remote sensing due to its ability to detect subtle changes on the earth's surface. Recently, diffusional denoising probabilistic models (DDPM) have demonstrated remarkable performance in the generative domain. Apart from their image generation capability, the denoising process in diffusion models can comprehensively account for the semantic correlation of spectral-spatial features in HSI, resulting in the retrieval of semantically relevant features in the original image. In this work, we extend the diffusion model's application to the HSI-CD field and propose a novel unsupervised HSI-CD with semantic correlation diffusion model (DiffUCD). Specifically, the semantic correlation diffusion model (SCDM) leverages abundant unlabeled samples and fully accounts for the semantic correlation of spectral-spatial features, which mitigates pseudo change between multi-temporal images arising from inconsistent imaging conditions. Besides, objects with the same semantic concept at the same spatial location may exhibit inconsistent spectral signatures at different times, resulting in pseudo change. To address this problem, we propose a cross-temporal contrastive learning (CTCL) mechanism that aligns the spectral feature representations of unchanged samples. By doing so, the spectral difference invariant features caused by environmental changes can be obtained. Experiments conducted on three publicly available datasets demonstrate that the proposed method outperforms the other state-of-the-art unsupervised methods in terms of Overall Accuracy (OA), Kappa Coefficient (KC), and F1 scores, achieving improvements of approximately 3.95%, 8.13%, and 4.45%, respectively. Notably, our method can achieve comparable results to those fully supervised methods requiring numerous annotated samples.

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