Bi-Objective Crop Mapping from Sentinel-2 Images Based on Multiple Deep Learning Networks
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Published:2023-07-06
Issue:13
Volume:15
Page:3417
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ISSN:2072-4292
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Container-title:Remote Sensing
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language:en
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Short-container-title:Remote Sensing
Author:
Song Weicheng1, Feng Aiqing2, Wang Guojie3ORCID, Zhang Qixia1, Dai Wen1ORCID, Wei Xikun1, Hu Yifan1, Amankwah Solomon Obiri Yeboah3, Zhou Feihong1, Liu Yi4
Affiliation:
1. Collaborative Innovation Center on Forecast and Evaluation of Metcorological Disasters, Nanjing University of Information Science & Technology (NUIST), Nanjing 210044, China 2. China Meteorological Administration Key Laboratory for Climate Prediction Studies, National Climate Center, Beijing 100081, China 3. School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology (NUIST), Nanjing 210044, China 4. School of Civil and Environmental Engineering, University of New South Wales (UNSW), Sydney 2052, Australia
Abstract
Accurate assessment of the extent of crop distribution and mapping different crop types are essential for monitoring and managing modern agriculture. Medium and high spatial resolution remote sensing (RS) for Earth observation and deep learning (DL) constitute one of the most major and effective tools for crop mapping. In this study, we used high-resolution Sentinel-2 imagery from Google Earth Engine (GEE) to map paddy rice and winter wheat in the Bengbu city of Anhui Province, China. We compared the performance of different popular DL backbone networks with the traditional machine learning (ML) methods, including HRNet, MobileNet, Xception, and Swin Transformer, within the improved DeepLabv3+ architecture, Segformer and random forest (RF). The results showed that the Segformer based on the combination of the Transformer architecture encoder and the lightweight multilayer perceptron (MLP) decoder achieved an overall accuracy (OA) value of 91.06%, a mean F1 Score (mF1) value of 89.26% and a mean Intersection over Union (mIoU) value of 80.70%. The Segformer outperformed other DL methods by combining the results of multiple evaluation metrics. Except for Swin Transformer, which was slightly lower than RF in OA, all DL methods significantly outperformed RF methods in accuracy for the main mapping objects, with mIoU improving by about 13.5~26%. The predicted images of paddy rice and winter wheat from the Segformer were characterized by high mapping accuracy, clear field edges, distinct detail features and a low false classification rate. Consequently, DL is an efficient option for fast and accurate mapping of paddy rice and winter wheat based on RS imagery.
Funder
Joint Research Project for Meteorological Capacity Improvement Meteorological Science and Technology Innovation Platform of China Meteorological Service Association China Meteorological Administration Special Foundation for Innovation and Development
Subject
General Earth and Planetary Sciences
Reference72 articles.
1. Winter wheat identification by integrating spectral and temporal information derived from multi-resolution remote sensing data;Zhang;J. Integr. Agric.,2019 2. Wang, Y.M., Zhang, Z., Feng, L.W., Du, Q.Y., and Runge, T. (2020). Combining Multi-Source Data and Machine Learning Approaches to Predict Winter Wheat Yield in the Conterminous United States. Remote Sens., 12. 3. An enhanced pixel-based phenological feature for accurate paddy rice mapping with Sentinel-2 imagery in Google Earth Engine;Ni;ISPRS J. Photogramm. Remote Sens.,2021 4. Li, S.L., Li, F.J., Gao, M.F., Li, Z.L., Leng, P., Duan, S.B., and Ren, J.Q. (2021). A New Method for Winter Wheat Mapping Based on Spectral Reconstruction Technology. Remote Sens., 13. 5. Huang, Q., Wu, W., Zhang, L., and Li, D. (2010, January 28–31). MODIS-NDVI-based crop growth monitoring in China agriculture remote sensing monitoring system. Proceedings of the 2010 Second IITA International Conference on Geoscience and Remote Sensing, Qingdao, China.
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