SDF2Net: Shallow to Deep Feature Fusion Network for PolSAR Image Classification

arXiv:2402.17672v1 Announce Type: new
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. Convolutional neural networks (CNNs) play a crucial role in capturing PolSAR image characteristics by leveraging kernel capabilities to consider local information and the complex-valued …

abstract arxiv challenges classification cs.cv data deep learning diverse eess.iv feature features fusion generate image images information interpretation network optical products radar solutions synthetic type