农业大数据学报2024，Vol.6Issue(1)：56-67,12.DOI:10.19788/j.issn.2096-6369.000010
多源数据融合的泛时空特征水稻深度学习提取
Pan-spatiotemporal Feature Rice Deep Learning Extraction Based on Multi-source Data Fusion
摘要
Abstract
Traditional methods of rice phenological phase feature extraction based on time-series remote sensing images require high temporal resolution,which is difficult to meet due to imaging conditions.Due to the different environmental conditions in different rice growing regions,the rice planting area extraction method based on single image has poor generalization ability.In this paper,similar optical and Synthetic Aperture Radar(SAR)data were selected to reduce the spatiotemporal information differences in rice planting area images.The spatial feature information of optical data and backscatter information of SAR data were effectively used to extract rice features by using a two-structure network model through pan-spatio-temporal feature fusion.Experiments show that the overall test accuracy of the training model validation set on the rice datasets of Sanjiang Plain and Feixi County is 95.66％,and the Kappa coefficient is 0.8805.The results of rice extraction in Nanchang City were in good agreement with the actual field boundaries,and the overall extraction accuracy was 86.78％,which proved the generalization ability and practicability of the pan-temporal feature model.关键词
泛时空特征/SAR数据/光学数据/特征融合/深度学习/水稻提取Key words
pan-temporal characteristics/SAR data/optical data/feature fusion/deep learning/rice extraction引用本文复制引用
杜家宽,李雁飞,孙嗣文,刘继东,江腾达..多源数据融合的泛时空特征水稻深度学习提取[J].农业大数据学报,2024,6(1):56-67,12.基金项目
农业遥感大数据并行处理技术研究应用(201400210300) （201400210300）
