Intelligent detection of crab ponds using remote sensing images based on a cooperative interpretation mechanism

doi:10.6046/zrzyyg.2022307

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Abstract

Digging ponds to raise crabs is a non-grain behavior of cultivated land, endangering national food security. However, the intelligent interpretation of remote sensing images targeting this behavior faces challenges such as laborious manual interpretation and low verification efficiency. Based on a cooperative interpretation mechanism, this study proposed an intelligent method for detecting crab ponds using remote sensing images. This method, integrating the HRNet segmentation network and the Swin-Transformer classification network models and combining manual verification, improved the detection accuracy and work efficiency. The application results of this method to Gaochun District, Nanjing City, Jiangsu Province show that the method for intelligent detection can automatically determine 83.4% of the spots for detection, with final identification accuracy of 0.972. The method proposed in this study can significantly reduce the identification difficulty and manual verification workload while improving the detection accuracy. Therefore, this study will provide a reliable solution for the accurate and efficient detection of non-grain surface features such as crab ponds.

Keywords cooperative interpretation mechanism HRNet Swin-Transformer crab pond detection non-grain

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TP79

Issue Date: 19 September 2023

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	Zhuoran JIANG
	Xinxin ZHOU
	Wei CAO
	Yahua WANG
	Changbin WU

Cite this article:

Zhuoran JIANG,Xinxin ZHOU,Wei CAO, et al. Intelligent detection of crab ponds using remote sensing images based on a cooperative interpretation mechanism[J]. Remote Sensing for Natural Resources, 2023, 35(3): 25-34.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022307 OR https://www.gtzyyg.com/EN/Y2023/V35/I3/25

Fig.1 Technology roadmap

Fig.2 HRNet network structure^[17]

Fig.3 Schematic diagram of feature fusion^[17]

Fig.4 Swin-Transformer network structure^[18]

Fig.5 Overview of the study area

Fig.6 Schematic diagram of HRNet sample

Fig.7 Schematic diagram of Swin-Transformer sample

Fig.8 Schematic diagram of manual interpretation sample

Fig.9 Partial results of crab pond prediction of HRNet model

Fig.10 Optimization of image segmentation results

Tab.1 Accuracy of Swin-Transformer model to predicts the crab pond

Tab.2 Reasoning results of Swin-Transformer model

Tab.3 Accuracy changes before and after collaborative interpretation mechanism

Fig.11 Final interpretation results of crab pond

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