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 this http URL, 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.
Abstract (translated)
扩散模型由于能够建模和生成复杂的数据分布，在多个领域内展示了卓越的性能。然而，当应用于极化合成孔径雷达（PolSAR）数据时，传统的实数值扩散模型在捕捉复杂的相位信息方面遇到挑战，并且难以保留精细的结构细节。为了解决这些问题，我们利用了轮廓波变换（Contourlet transform），该技术提供了丰富的多尺度和多方向表示，特别适合用于极化合成孔径雷达图像处理。 为此，我们提出了一种基于轮廓波域中的结构知识引导型复扩散模型，以提高PolSAR图像分类的准确性。具体而言，首先使用复数轮廓波变换将数据分解为低频和高频子带，从而能够提取统计特性和边界特征。接着设计了一个知识引导下的复扩散网络，用于建模低频分量的统计特性。在此过程中，利用高频系数中的结构信息来指导扩散过程，以提高边缘保留能力。此外，通过联合学习多尺度和多方向的高频特征进一步提升分类精度。 实验结果表明，在三个真实的PolSAR数据集上，我们的方法超越了现有最先进的技术，特别是在复杂地形中保持边界细节和区域同质性方面表现尤为突出。
URL
https://arxiv.org/abs/2507.05666
