International Journal of Applied Earth Observations and Geoinformation (Dec 2022)
WaterHRNet: A multibranch hierarchical attentive network for water body extraction with remote sensing images
Abstract
Water is a kind of vital natural resource, which acts as the lifeblood of the ecosystem and the energy source for the living and production activities of humans. Regularly mapping the conditions of water resources and taking effective measures to prevent them from pollutions and shortages are very important and necessary to maintain the sustainability of the ecosystem. As a preliminary step for image-based water resource analysis, the complete recognition and accurate extraction of water bodies are important prerequisites in many applications. Nevertheless, due to the issues of topology diversities, appearance variabilities, and land cover interferences, there is still a large gap to achieve the human-level water bodies interpretation quality. This paper presents a hierarchical attentive high-resolution network, abbreviated as WaterHRNet, for extracting water bodies from remote sensing imagery. First, by building a multibranch high-resolution feature extractor integrated with global feature semantics aggregation, the WaterHRNet behaves laudably to supply high-quality, strong-semantic feature representations. Furthermore, by inlaying an effective feature attention scheme with the comprehensive exploitation of both the spatial and channel feature significances, the WaterHRNet is forced to strengthen the semantic-determinate, task-aware feature encodings. In addition, by designing a hierarchical processing principle with the progressive enhancement of category-attentive feature semantics, the WaterHRNet performs effectively to export semantic-discriminative, target-oriented feature representations for precise water body segmentation. The WaterHRNet is elaborately verified both quantitatively and qualitatively on three remote sensing datasets. Evaluation results show that the WaterHRNet achieves an average precision of 98.44%, average recall of 97.84%, average IoU of 96.35%, and average F1-score of 98.14%. Comparative analyses also demonstrate the superior performance and excellent feasibility of the WaterHRNet in segmenting water bodies.
