PolSAR Image Classification
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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.
PolSAR Image Classification using a Hybrid Complex-Valued Network (HybridCVNet)
arXiv:2605.31137v1 Announce Type: new Abstract: Recently, convolutional neural networks (CNNs) have become popular for image classification due to their effectiveness in computer vision tasks. Now, researchers are exploring the potential of vision transformers (ViTs) in remote sensing and Earth observation. However, traditional Real-Valued networks often overlook important phase information in Complex-Valued (CV) data like polarimetric synthetic aperture radar (PolSAR) data.