Refined Water-Body Types Mapping Using a Water-Scene Enhancement Deep Models by Fusing Optical and SAR Images

Abstract

Water is an important element in the ecological environment, and different types of water (e.g., rivers, lakes, and ponds) have different impacts on the ecology. The extraction and classification of different types of water bodies has significant implications for the water resource management and water environment monitoring. Current research on the water-body types classification is relatively limited compared to water body extraction. Existing methods typically adopt a two-stage architecture, where the first stage extracts water bodies at the pixel level, and the second stage classifies the water bodies into different types using rule-based thresholds classifier and morphological features at object level. However, methods in the second stage suffer from overfitting, lack of robustness, and confusion in object segmentation. Despite these challenges, the deep learning methods could capture the high-level semantic features, which are effective for the classification of different types of water bodies. In this article, a novel water-scene enhancement deep model (WSEDM) was proposed for identifying multiple types of water bodies. The WSEDM consists of a pixel-wise water body extraction using Edge-Otsu and a patch-wise water-body types classification through deep learning model. In order to improve the accuracy of patch-wise water body classification, a novel multimodal feature fusion network (CASANet) was designed for the fusing of optical and synthetic aperture radar images. The water-body types classification was conducted on three international wetland cities in the urban agglomeration in the middle reaches of the Yangtze River. The 10-m water-body types map achieved an overall accuracy of 94.6%. The proposed CASANet is also validated through comparison and transferability experiments, which further confirmed the superior performance.