🌟 DDSD-GANet: Adversarial Optical-Prior Transfer with Multiresolution Anisotropic Decoding for Coherence-Structured SAR Segmentation This project implements the Dual-Driven Spectral-Directional Cross-Modal Generative Adversarial Network (DDSD-GANet) proposed in the paper "Adversarial Optical-Prior Transfer with Multiresolution Anisotropic Decoding for Coherence-Structured SAR Segmentation".

DDSD-GANet is an advanced framework for Synthetic Aperture Radar (SAR) image semantic segmentation. It is designed to address the challenges inherent in SAR imagery, specifically multiplicative speckle noise, absence of optical-domain semantic priors (geometric semantic deficiency), and intrinsic scale inconsistency. By incorporating adversarial cross-modal learning and an innovative decoder structure , DDSD-GANet achieves significant performance improvements in the structured segmentation of SAR targets, such as buildings, vessels, and sea-land boundaries.