With the swift advancement of deep learning, significant strides have been made in Polarimetric Synthetic Aperture Radar (PolSAR) image classification, particularly with the advent of diffusion models that allow for explicit class probability modeling. However, existing diffusion models have yet to fully leverage the rich polarimetric characteristics of PolSAR images. To address this, we propose the Multi-level Conditional Diffusion (MCDiff) model for PolSAR image classification, incorporating three key strategies. Firstly, a Prior Learning (PL) module is constructed to capture scattering characteristics across all three polarization basis parameter spaces, providing conditional guidance for the diffusion model. Secondly, a Multi-scale and Multi-dimensional noise Prediction (MMP) module is designed to reduce the information loss when noisy labels and image features of different dimensions are fused to predict noise. Finally, a Multi-level high-order Statistical feature Learning (MSL) module is introduced to aid in the additive Gaussian noise prediction of noisy labels while mitigating the impact of PolSAR images' multiplicative speckle noise on the prediction. Experimental results on three benchmark datasets confirm MCDiff's ability to achieve high-performance explicit class probability modeling for PolSAR images among the compared methods

The sensitivity and dependence of polarization and scattering on target orientation and radar incident angle pose a significant challenge in interpreting PolSAR images. Previous studies have emphasized the potential advantages of compensating for target orientation and acquiring diverse scattering information by rotating the polarimetric matrix. To fully exploit inter-pixel polarization information and achieve consistency in the targets' polarization orientation angle (POA), we propose a transformer-based framework that combines POA align with cyclic channel attention for PolSAR image classification. First, we select relevant features and introduce an implicit layer to facilitate unsupervised learning of POA, guiding the alignment of target orientation. Second, considering the inherent correlation between input POA channels and the necessity for more rational and efficient utilization of channel information, we devise a lightweight channel attention module with local cross-channel interaction and a cyclic padding strategy. Finally, this article is devoted to presenting a unified global modeling framework based on transformer. The primary objective is to overcome the limitations of local modeling capability in CNNs and comprehensively capture distinctions between polarization channels and intricate relationships among polarimetric information across pixels. Extensive experiments and analyses have demonstrated the robustness and effectiveness of the proposed method.

With the advancement of polarimetric synthetic aperture radar (PolSAR) imaging technology and the growing demand for image interpretation, extracting meaningful land cover information from PolSAR images has become a key research focus. To address the segmentation challenge, we propose an innovative method. First, features from co-polarization and cross-polarization channels are separately used as dual inputs, and a cross-attention mechanism effectively fuses these features to capture correlations between different polarization information. Second, a diffusion framework is employed to jointly model target features and class probabilities, aiming to improve segmentation accuracy by learning and fitting the probabilistic distribution of target labels. Finally, experimental results demonstrate that the proposed method achieves superior performance in PolSAR image segmentation, effectively managing complex polarization relationships while offering robustness and broad application potential.