Nonconvex-NLTV Regularization-Based SAR Image Feature Enhancement with Water Body Information Extraction Using QILU-1 SAR Data

Abstract

Synthetic aperture radar (SAR) images have been widely used in water body information extraction. However, SAR images suffer from speckles and the additive noise, which affect the performance of automatic information extraction. Thus, we propose the nonconvex-nonlocal total variation (NLTV) regularization to suppress speckles and the additive noise, and improve the performance of water body information extraction using the enhanced images. Experiments using Qilu-1 (QL-1) SAR data verify the effectiveness of the method.