Intelligent Aquatic Vegetation Extraction Utilizing a Multispatial Resolution Feature Extractor Based on High-Spatial-Resolution Remote Sensing Images

Aquatic vegetation serves as a critical environmental indicator in Chinese mitten crab (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Eriocheir sinensis</i>) aquaculture systems, with its distinctive distribution patterns enabling differentiation of crab farming zones from other aquatic environments. However, extracting vegetation from small-scale, fragmented aquaculture areas presents significant challenges due to limited specialized datasets, heterogeneous vegetation distribution, and complex spectral interactions between water and vegetation. This study addresses these limitations by developing a comprehensive dataset and proposing a novel Multi-scale Resolution Interaction Network (MRINet) for automated aquatic vegetation extraction using high-resolution satellite imagery. MRINet integrates several innovative components: bottleneck and dense connection modules for efficient feature propagation, a proprietary multi-scale feature interaction extractor for enhanced global feature sampling, and a multi-scale loss fusion strategy for precise vegetation identification across varying geographic scales. Experimental validation on GaoJing (GJ) and GaoFen-2 (GF-2) datasets demonstrates MRINet's superior performance, achieving 84.43% precision and 98.48% accuracy on GJ imagery, and 79.97% precision and 94.37% accuracy on GF-2 imagery, while maintaining computational efficiency compared to mainstream segmentation methods. Large-scale application across Yixing, Xinghua, and Gaochun regions reveals strong spatial correlation between extracted vegetation distribution and crab aquaculture areas. Notably, Gaochun exhibited 21,958,871 m² vegetation coverage, representing 65.88% of the official 33,330,000 m² aquaculture zone, aligning closely with industry standards of 60-70% vegetation coverage in crab ponds. This research establishes a robust methodology for automated identification and quantitative assessment of aquatic vegetation in Chinese mitten crab aquaculture regions, providing essential support for precision aquaculture management and sustainable industry development.