A full resolution deep learning network for paddy rice mapping using Landsat data

作者：Lang Xia, Fen Zhao, Jin Chen, Le Yu, Miao Lu, Qiangyi Yu, Shefang Liang, Lingling Fan, Xiao Sun, Shangrong Wu, Wenbin Wu, Peng Yang · 发表于：ISPRS Journal of Photogrammetry and Remote Sensing · 年份：2022 · DOI：10.1016/j.isprsjprs.2022.10.005 · 被引用次数：72 · 研究领域：Remote Sensing in Agriculture、Remote-Sensing Image Classification、Remote Sensing and Land Use

Rice is the most important food crop in the developing world, and more than half of the global population consumes it as a staple food. Mapping the area of rice cultivation in a timely and accurate manner is essential to ensure food safety and evaluate its environmental impact. Deep learning performs very well in high-resolution remote sensing classification; however, due to lack of high-quality training datasets and spatial semantic information of Landsat data, paddy rice mapping based on deep learning and Landsat data has received less attention. In this study, we constructed the first large-scale training dataset and a deep learning network, named full resolution network (FR-Net), for mapping paddy rice based on Landsat 8 OLI data. The pixel-wise annotated dataset is composed of 64 Landsat 8 OLI scenes covering the main areas producing rice in northeast China. To overcome the coarse segmentation borders resulting from other deep learning models, and especially with the low-resolution Landsat data, a new multi-resolution fusion unit (MRFU) was proposed to fuse different resolution streams and maintain the high-resolution streams of the model. In comparison with other models, the FR-Net acquired the highest accuracy, with MCC of 0.893 and F1 score of 0.898, and in particular, the results of different band combinations showed that the FR-Net perform better for feature extraction than other models. Without using the sensitive bands, i.e., short-wave infrared 1 and 2, the MCC o...