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Intelligent Aquatic Vegetation Extraction Utilizing a Multi-Spatial Resolution Feature Extractor Based on High-Spatial-Resolution Remote Sensing Images
Author(s) -
Peiyu Dai,
Xing Mao,
Jing Jin,
Weiguo Li,
Xin Zhang,
Nan Li,
Jianbin Dong,
Ni Ren
Publication year - 2025
Publication title -
ieee journal of selected topics in applied earth observations and remote sensing
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 1.246
H-Index - 88
eISSN - 2151-1535
pISSN - 1939-1404
DOI - 10.1109/jstars.2025.3614188
Subject(s) - geoscience , signal processing and analysis , power, energy and industry applications
Aquatic vegetation serves as a critical environmental indicator in Chinese mitten crab ( Eriocheir sinensis ) 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.

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