Research on Remote Sensing Intelligent Extraction Method of Tropical Rice Planting Area based on Deep Learning:A Case Study of Haikou City,Hainan Province
The cultivation and breeding of rice in Hainan,one of the primary tropical regions in China,play a crucial role in meeting the country's demand for this essential food crop.Currently,there are several challenges in monitoring rice cultivation in the tropical region of Hainan,including limited automation,excessive work-load,and low accuracy.In this study,we selected Haikou City in Hainan Province as our experimental area.By utilizing high-resolution multi-spectral satellite remote sensing images such as Jilin-1,Beijing-2,WorldView,and Gaojing-1 along with field verification data,we established a comprehensive database consisting of multi-source and multi-scale samples to accurately identify rice planting areas within the tropical region of Hainan.We employed the DeepLab-V3+convolutional neural network model for training purposes and proposed an intelli-gent remote sensing interpretation method specifically tailored for identifying rice planting areas within the tropi-cal region.Experimental results demonstrated that our approach achieved an impressive accuracy rate of 81.9％with a recall rate of 86.7％when extracting rice intelligently based on the DeepLab-V3+convolutional neural network model.These findings highlight that by training a convolutional neural network model using our inter-pretive sample database,it becomes possible to accurately extract regions where tropical rice is cultivated from high-resolution multi-spectral remote sensing imagery—a methodology that can serve as a valuable reference for future studies on extracting information related to tropical rice cultivation.
Deep learningConvolutional neural networksRice extractionMultispectralTropics
万方数据
维普
