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
Pengliang Wei, Dengfeng Chai, Ran Huang, Dailiang Peng, Tao Lin, Jinming Sha, Weiwei Sun, Jingfeng Huang
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.