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SDF2Net: Shallow to Deep Feature Fusion Network for PolSAR Image Classification
arXiv:2402.17672v2 Announce Type: replace Abstract: Polarimetric synthetic aperture radar (PolSAR) images encompass valuable information that can facilitate extensive land cover interpretation and generate diverse output products. Extracting meaningful features from PolSAR data poses challenges distinct from those encountered in optical imagery. Deep learning (DL) methods offer effective solutions for overcoming these challenges in PolSAR feature extraction.