Water-Body Type Classification in Dual PolSAR Imagery Using a Two-Step Deep-Learning Method

Abstract

Water-body type classification plays a vital role in ecological conservation, water resource management, and urban planning. Accurate classification can aid decision-makers in understanding the functions of different water-body types, providing key information for urban planning and promoting harmony between human activities and the natural environment. Despite extensive research in the field of water-body segmentation, exploration in the water-body type classification community is not as widespread. Therefore, this article proposes a novel water-body type classification method based on a two-step deep learning model, decomposing water-body type classification into waterbody segmentation and water-body type identification. Especially, this method constructs a unique data strategy by organically integrating backscatter features, polarimetric features, and DEM features, providing the model with rich and comprehensive information. In the first step, the segmentation network uses the fused feature to extract all water-body from Synthetic Aperture Radar (SAR) images. Subsequently, the extracted water-body are combined with the input data, forming a multi-feature input for the identification network to distinguish between natural and artificial water-body. During this process, a swarm intelligence optimization algorithm is employed to explore the optimal hyperparameters of the network, including those of the segmentation and identification networks. Finally, the proposed method is assessed using extensive experiments on water-body segmentation tasks, water-body type identification tasks, and joint water-body type classification tasks. This article not only provides a new perspective in the field of water-body type classification but also demonstrates the immense potential of deep learning network hyperparameter optimization and feature fusion in solving such problems.