AI 中文总结
该研究针对PolSAR图像分类问题，提出WGDNet深度网络，集成可学习威沙特卷积、方向先验聚合模块和GAnet，实现多尺度统计边缘特征提取等，在四个数据集评估中分类精度和边界保真度超现有方法。
AI 中文摘要
极化合成孔径雷达（PolSAR）分类是全天候地球观测的基础。传统的威沙特方法依赖于适应性有限的固定手工算子，而主流深度网络忽略了PolSAR固有的威沙特散射统计。此外，固定卷积窗口无法捕捉多尺度、多方向的地形模式，影响边界检测和小目标特征。为缓解这些缺点，我们提出了WGDNet，一种威沙特引导的几何感知深度网络。它集成了三个核心设计：用于多尺度统计边缘特征提取的带方向核的可学习威沙特卷积；估计主导局部方向和置信度以自适应细化方向威沙特输出的方向先验聚合模块；GAnet，一种尺度方向自适应几何感知卷积，动态重塑采样网格以模拟各向异性地形并保留精细细节。我们的贡献在于可学习的威沙特统计建模、方向先验特征聚合和几何自适应卷积。在四个真实PolSAR数据集上的评估验证了WGDNet在分类精度和边界保真度方面优于现有最先进方法。
英文摘要
Polarimetric Synthetic Aperture Radar (PolSAR) classification underpins all-weather Earth observation. Conventional Wishart methods depend on rigid handcrafted operators with limited adaptability, while mainstream deep networks ignore PolSAR native Wishart scattering statistics. Additionally, fixed convolution windows fail to capture multi-scale, multi-directional terrain patterns, harming boundary detection and small-object characterization. To mitigate these drawbacks, we propose WGDNet, a Wishart-guided geometric-aware deep network. It integrates three core designs: (1) learnable Wishart convolutions with directional kernels for multi-scale statistical edge feature extraction; (2) an orientation-prior aggregation module that estimates dominant local directions and confidences to refine directional Wishart outputs adaptively; (3) GAnet, a scale-direction adaptive geometric-aware convolution that dynamically reshapes sampling grids to model anisotropic terrain and retain fine details. Our contributions lie in learnable Wishart statistical modeling, orientation-prior feature aggregation, and geometry-adaptive convolution. Evaluations across four real PolSAR datasets verify WGDNet surpasses existing state-of-the-art approaches in classification accuracy and boundary fidelity.
