用可学习的统计模型提升极化雷达图像分类精度
WGDnet: Wishart-guided Geometric-aware Deep Network for PolSAR Image Classification
- 引入可学习的Wishart卷积，捕捉多尺度方向特征
- 在四个真实数据集上准确率超越现有方法
- 适合需要精细边界识别的遥感图像分析
极化合成孔径雷达（PolSAR）图像分类支撑全天候地球观测。传统Wishart方法依赖固定手工算子，适应性差；主流深度网络忽略PolSAR固有的Wishart散射统计特性。此外，固定卷积窗口难以捕捉多尺度、多方向地形模式，影响边界检测与小目标刻画。为此，我们提出WGDNet：一种受Wishart引导的几何感知深度网络。其包含三个核心设计：(1) 可学习的、带方向性的卷积核，用于多尺度统计边缘特征提取；(2) 方向先验聚合模块，估计局部主导方向与置信度，自适应优化方向性Wishart输出；(3) GAnet，一种尺度-方向自适应的几何感知卷积，动态重设采样网格以建模各向异性地形并保留细节。贡献在于可学习的Wishart统计建模、方向先验特征聚合及几何自适应卷积。在四个真实PolSAR数据集上的评估表明，WGDNet在分类精度和边界保真度上均优于现有最先进方法。
原文摘要 · Abstract (English)
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.
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