R-Unet: A Deep Learning Model for Rice Extraction in Rio Grande do Sul, Brazil

Abstract

Rice is one of the world’s three major food crops, second only to sugarcane and corn in output. Timely and accurate rice extraction plays a vital role in ensuring food security. In this study, R-Unet for rice extraction was proposed based on Sentinel-2 and time-series Sentinel-1, including an attention-residual module and a multi-scale feature fusion (MFF) module. The attention-residual module deepened the network depth of the encoder and prevented information loss. The MFF module fused the high-level and low-level rice features at channel and spatial scales. After training, validation, and testing on seven datasets, R-Unet performed best on the test samples of Dataset 07, which contained optical and synthetic aperture radar (SAR) features. Precision, intersection, and union (IOU), F1-score, and Matthews correlation coefficient (MCC) were 0.948, 0.853, 0.921, and 0.888, respectively, outperforming the baseline models. Finally, the comparative analysis between R-Unet and classic models was completed in Dataset 07. The results showed that R-Unet had the best rice extraction effect, and the highest scores of precision, IOU, MCC, and F1-score were increased by 5.2%, 14.6%, 11.8%, and 9.3%, respectively. Therefore, the R-Unet proposed in this study can combine open-source sentinel images to extract rice timely and accurately, providing important information for governments to implement decisions on agricultural management.