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arXiv · 2507.05666

Knowledge-guided Complex Diffusion Model for PolSAR Image Classification in Contourlet Domain

Junfei Shi·Yu Cheng·Haiyan Jin·Junhuai Li·Zhaolin Xiao·Maoguo Gong·Weisi Lin
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Abstract

Diffusion models have demonstrated exceptional performance across various domains due to their ability to model and generate complicated data distributions. However, when applied to PolSAR data, traditional real-valued diffusion models face challenges in capturing complex-valued phase information.Moreover, these models often struggle to preserve fine structural details. To address these limitations, we leverage the Contourlet transform, which provides rich multiscale and multidirectional representations well-suited for PolSAR imagery. We propose a structural knowledge-guided complex diffusion model for PolSAR image classification in the Contourlet domain. Specifically, the complex Contourlet transform is first applied to decompose the data into low- and high-frequency subbands, enabling the extraction of statistical and boundary features. A knowledge-guided complex diffusion network is then designed to model the statistical properties of the low-frequency components. During the process, structural information from high-frequency coefficients is utilized to guide the diffusion process, improving edge preservation. Furthermore, multiscale and multidirectional high-frequency features are jointly learned to further boost classification accuracy. Experimental results on three real-world PolSAR datasets demonstrate that our approach surpasses state-of-the-art methods, particularly in preserving edge details and maintaining region homogeneity in complex terrain.

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Junfei Shi, Yu Cheng, Haiyan Jin, Junhuai Li, Zhaolin Xiao, Maoguo Gong, Weisi Lin. 2025-07-08. Knowledge-guided Complex Diffusion Model for PolSAR Image Classification in Contourlet Domain. https://arxiv.org/abs/2507.05666

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