多源数据融合的泛时空特征水稻深度学习提取

doi:10.19788/j.issn.2096-6369.000010

Abstract

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.

Key words: pan-temporal characteristics, SAR data, optical data, feature fusion, deep learning, rice extraction

DU JiaKuan, LI YanFei, SUN SiWen, LIU JiDong, JIANG TengDa. Pan-spatiotemporal Feature Rice Deep Learning Extraction Based on Multi-source Data Fusion[J].Journal of Agricultural Big Data, 2024, 6(1): 56-67.

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精度指标	OA	IoU	Kappa
HRnet	0.9326	0.7981	0.8621
Ours	0.9566	0.8086	0.8805
提升	0.0240	0.0105	0.0184

Pan-spatiotemporal Feature Rice Deep Learning Extraction Based on Multi-source Data Fusion

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