WGDnet: Wishart-guided Geometric-aware Deep Network for PolSAR Image Classification
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 o...
| Authors | Junfei Shi, Haojia Zhang, Yu Cheng et al. |
|---|---|
| Primary category | cs.CV |
| Published | 26 July 2026 |
| Last updated at source | 26 July 2026 |
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