{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T04:31:32Z","timestamp":1768797092238,"version":"3.49.0"},"reference-count":51,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T00:00:00Z","timestamp":1641859200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42171314"],"award-info":[{"award-number":["42171314"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Eramus+Programme of the European Union","award":["598838-EPP-1-2018-EL-EPPKA2-CBHE-JP"],"award-info":[{"award-number":["598838-EPP-1-2018-EL-EPPKA2-CBHE-JP"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A deep semantic segmentation model-based method can achieve state-of-the-art accuracy and high computational efficiency in large-scale crop mapping. However, the model cannot be widely used in actual large-scale crop mapping applications, mainly because the annotation of ground truth data for deep semantic segmentation model training is time-consuming. At the operational level, it is extremely difficult to obtain a large amount of ground reference data by photointerpretation for the model training. Consequently, in order to solve this problem, this study introduces a workflow that aims to extract rice distribution information in training sample shortage regions, using a deep semantic segmentation model (i.e., U-Net) trained on pseudo-labels. Based on the time series Sentinel-1 images, Cropland Data Layer (CDL) and U-Net model, the optimal multi-temporal datasets for rice mapping were summarized, using the global search method. Then, based on the optimal multi-temporal datasets, the proposed workflow (a combination of K-Means and random forest) was directly used to extract the rice-distribution information of Jiangsu (i.e., the K\u2013RF pseudo-labels). For comparison, the optimal well-trained U-Net model acquired from Arkansas (i.e., the transfer model) was also transferred to Jiangsu to extract local rice-distribution information (i.e., the TF pseudo-labels). Finally, the pseudo-labels with high confidences generated from the two methods were further used to retrain the U-Net models, which were suitable for rice mapping in Jiangsu. For different rice planting pattern regions of Jiangsu, the final results showed that, compared with the U-Net model trained on the TF pseudo-labels, the rice area extraction errors of pseudo-labels could be further reduced by using the U-Net model trained on the K\u2013RF pseudo-labels. In addition, compared with the existing rule-based rice mapping methods, he U-Net model trained on the K\u2013RF pseudo-labels could robustly extract the spatial distribution information of rice. Generally, this study could provide new options for applying a deep semantic segmentation model to training sample shortage regions.<\/jats:p>","DOI":"10.3390\/rs14020328","type":"journal-article","created":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T20:33:04Z","timestamp":1641933184000},"page":"328","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Rice Mapping in Training Sample Shortage Regions Using a Deep Semantic Segmentation Model Trained on Pseudo-Labels"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4432-1338","authenticated-orcid":false,"given":"Pengliang","family":"Wei","sequence":"first","affiliation":[{"name":"Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental Resource Sciences, Zhejiang University, Hangzhou 310058, China"},{"name":"Institute of Applied Remote Sensing and Information Technology, Zhejiang University, Hangzhou 310058, China"},{"name":"Key Laboratory of Agricultural Remote Sensing and Information Systems, Hangzhou 310058, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0226-8492","authenticated-orcid":false,"given":"Ran","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}]},{"given":"Tao","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4627-6021","authenticated-orcid":false,"given":"Jingfeng","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental Resource Sciences, Zhejiang University, Hangzhou 310058, China"},{"name":"Institute of Applied Remote Sensing and Information Technology, Zhejiang University, Hangzhou 310058, China"},{"name":"Key Laboratory of Agricultural Remote Sensing and Information Systems, Hangzhou 310058, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.neunet.2017.07.017","article-title":"A patch-based convolutional neural network for remote sensing image classification","volume":"95","author":"Sharma","year":"2017","journal-title":"Neural Netw."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Tian, F., Wu, B., Zeng, H., Zhang, X., and Xu, J. 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