Rice mapping based on Sentinel-1 images using the coupling of prior knowledge and deep semantic segmentation network: A case study in Northeast China from 2019 to 2021

Abstract

Usually, continuous effective optical observation data collected from a single-sensor within one year is not available, most studies conducted rice mapping in Northeast China using optical remote sensing images collected from multi-years and multi-sensors. Meanwhile, the accuracies of mapping results are limited by the employed traditional machine learning algorithms, which cannot fully exploit the deep abstract features contained in the multi-temporal images. Consequently, this paper developed a deep learning framework for large-scale rice mapping based on multi-temporal Sentinel-1 images in practice. It was achieved via semantic segmentation based on coupling of U-Net and prior knowledge (i.e., the coupled U-Net). Classification experiments were conducted in regions covered by complete and incomplete multi-temporal Sentinel-1 images to validate advantages of the coupled U-Net. Besides, feature visualization experiments were conducted, and the results showed that the coupled U-Net could further robustly learn deep abstract feature that was more suitable to express land covers. Finally, rice maps of Northeast China in different years were produced by the coupled U-Net and Sentinel-1 images. For the results of 2021, both rice producer’s and user’s accuracies exceeded 85%, while its overall accuracy and F1-score were higher than 0.9. For the results of 2019 and 2020, the rice areas of Northeast China extracted from Sentinel-1 images were 4.0% and 4.9% less than those of the subnational statistics data. This study provided a viable option toward practical large-scale rice mapping based on multi-temporal Sentinel-1 images through the coupling of prior knowledge and deep learning technology.