Remote Sensing, Vol. 17, Pages 3897: A Novel U-Shaped Network Combined with a Hierarchical Sparse Attention Mechanism for Coastal Aquaculture Area Extraction in a Complex Environment

Remote Sensing doi: 10.3390/rs17233897

Authors:
Chengyi Wang
Yuyang Zhao
Lu Li
Tianyi Liu

Aquaculture pond extraction based on remote sensing (RS) plays a pivotal role in coastal resource utilization and production management. However, most existing studies have focused on limited coastal aquaculture pond extraction and neglected the extraction around saltpans. There are two key challenges in aquaculture pond extraction. Firstly, aquaculture ponds are difficult to accurately extract owing to the spectral and spatial similarities with evaporation ponds and brine concentration ponds within saltpans. Secondly, refining and delineating the boundaries of aquaculture ponds remains challenging. To address these issues, we propose a novel deep learning neural network, namely the U-shaped Network with Hierarchical Sparse Attention (HSAUNet), for coastal aquaculture pond extraction. We proposed the Dycross Sample Module to dynamically generate learnable offsets, which empower our model to accurately capture edge-specific information under the guidance of lower-level feature maps, thus improving the precise perceptiveness of aquaculture boundaries. The Sparse Attention Module with rolling mechanism is proposed to effectively capture global semantic relationships and contextual information in different directions, achieving clear differentiation between aquaculture ponds and evaporation or brine ponds within saltpans. Our datasets are derived from the multispectral Sentinel-2 imagery satellite data including aquaculture ponds around saltpans such as the Changlu Hangu, Huaibei, and Yinggehai salt fields and also some other coastal aquaculture areas such as Shanwei Changsha Bay (Guangdong province) and Dalian Biliuhe Bay (Liaoning province). Experimental results demonstrate that HSAUNet outperforms other state-of-the-art methods on test datasets, achieving an intersection over union (IoU) of 93.42%, which exceeds the highest scores of Deeplabv3+ with a IoU of 92.97%. Our proposed method greatly facilitates and serves as a valuable reference for resource management authorities in monitoring aquaculture ponds.

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Chengyi Wang www.mdpi.com