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

文献类型： 外文期刊

第一作者： Xia, Lang

作者： Xia, Lang;Zhao, Fen;Lu, Miao;Yu, Qiangyi;Liang, Shefang;Fan, Lingling;Sun, Xiao;Wu, Shangrong;Wu, Wenbin;Yang, Peng;Chen, Jin;Yu, Le;Yang, Peng;Yang, Peng

作者机构：

关键词： Paddy rice; Deep learning; Resolution fusion; Landsat; Training dataset

期刊名称：ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING （ 影响因子：11.774； 五年影响因子：12.123 ）

ISSN： 0924-2716

年卷期： 2022 年 194 卷

页码：

收录情况： SCI

摘要： 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 in-formation 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 of FR-Net was slightly decreased by 4.14%, smaller than other models, e.g., 8.51% for U-Net and 11.76% for RS-Net etc. The comparison showed the proposed FR-Net presented highest spatial precision among all these models, by which a fine boundary could be obtained for Landsat data to achieve high quality mapping result. The analysis of the performance in different temporal showed that FR-Net performed better at the beginning or the ending of growth stages, and the omission or false alarm main occurred in small size patches or the boundaries. In all, low-resolution characteristics of Landsat data should be more extensively investigated when developing and using deep learning in classification.

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