{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T09:28:15Z","timestamp":1766050095010,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,16]],"date-time":"2022-08-16T00:00:00Z","timestamp":1660608000000},"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":["41921001","42071419","1610132020016","CAAS-ZDRW202201","2019YFA0607400","2019FY202501"],"award-info":[{"award-number":["41921001","42071419","1610132020016","CAAS-ZDRW202201","2019YFA0607400","2019FY202501"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Funds for Central Non-profit Scientific Institution","award":["41921001","42071419","1610132020016","CAAS-ZDRW202201","2019YFA0607400","2019FY202501"],"award-info":[{"award-number":["41921001","42071419","1610132020016","CAAS-ZDRW202201","2019YFA0607400","2019FY202501"]}]},{"DOI":"10.13039\/501100012421","name":"Agricultural Science and Technology Innovation Program","doi-asserted-by":"publisher","award":["41921001","42071419","1610132020016","CAAS-ZDRW202201","2019YFA0607400","2019FY202501"],"award-info":[{"award-number":["41921001","42071419","1610132020016","CAAS-ZDRW202201","2019YFA0607400","2019FY202501"]}],"id":[{"id":"10.13039\/501100012421","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["41921001","42071419","1610132020016","CAAS-ZDRW202201","2019YFA0607400","2019FY202501"],"award-info":[{"award-number":["41921001","42071419","1610132020016","CAAS-ZDRW202201","2019YFA0607400","2019FY202501"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Project of Special Investigation on Basic Resources of Science and Technology","award":["41921001","42071419","1610132020016","CAAS-ZDRW202201","2019YFA0607400","2019FY202501"],"award-info":[{"award-number":["41921001","42071419","1610132020016","CAAS-ZDRW202201","2019YFA0607400","2019FY202501"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Information on crop spatial distribution is essential for agricultural monitoring and food security. Classification with remote-sensing time series images is an effective way to obtain crop distribution maps across time and space. Optimal features are the precondition for crop classification and are critical to the accuracy of crop maps. Although several approaches are available for extracting spectral, temporal, and phenological features for crop identification, these methods depend heavily on domain knowledge and human experiences, adding uncertainty to the final crop classification. This study proposed a novel Genetic Programming (GP) approach to learning high-level features from time series images for crop classification to address this issue. We developed a new representation of GP to extend the GP tree\u2019s width and depth to dynamically generate either fixed or flexible informative features without requiring domain knowledge. This new GP approach was wrapped with four classifiers, i.e., K-Nearest Neighbor (KNN), Decision Tree (DT), Naive Bayes (NB), and Support Vector Machine (SVM), and was then used for crop classification based on MODIS time series data in Heilongjiang Province, China. The performance of the GP features was compared with the traditional features of vegetation indices (VIs) and the advanced feature learning method Multilayer Perceptron (MLP) to show GP effectiveness. The experiments indicated that high-level features learned by GP improved the classification accuracies, and the accuracies were higher than those using VIs and MLP. GP was more robust and stable for diverse classifiers, different feature numbers, and various training sample sets compared with classification using VI features and the classifier MLP. The proposed GP approach automatically selects valuable features from the original data and uses them to construct high-level features simultaneously. The learned features are explainable, unlike those of a black-box deep learning model. This study demonstrated the outstanding performance of GP for feature learning in crop classification. GP has the potential of becoming a mainstream method to solve complex remote sensing tasks, such as feature transfer learning, image classification, and change detection.<\/jats:p>","DOI":"10.3390\/rs14163982","type":"journal-article","created":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T03:15:27Z","timestamp":1660706127000},"page":"3982","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Genetic Programming for High-Level Feature Learning in Crop Classification"],"prefix":"10.3390","volume":"14","author":[{"given":"Miao","family":"Lu","sequence":"first","affiliation":[{"name":"Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs\/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"},{"name":"School of Engineering and Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2758-6067","authenticated-orcid":false,"given":"Ying","family":"Bi","sequence":"additional","affiliation":[{"name":"School of Engineering and Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand"},{"name":"School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bing","family":"Xue","sequence":"additional","affiliation":[{"name":"School of Engineering and Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiong","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengjie","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Engineering and Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanbing","family":"Wei","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs\/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7999-9763","authenticated-orcid":false,"given":"Peng","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs\/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenbin","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs\/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1038\/nature21694","article-title":"Finding pathways to national-scale land-sector sustainability","volume":"544","author":"Gao","year":"2017","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1038\/nature25785","article-title":"Pursuing sustainable productivity with millions of smallholder farmers","volume":"555","author":"Cui","year":"2018","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1038\/s41586-019-1316-y","article-title":"National food production stabilized by crop diversity","volume":"571","author":"Renard","year":"2019","journal-title":"Nature"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.rse.2017.07.015","article-title":"Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications","volume":"199","author":"Veloso","year":"2017","journal-title":"Remote Sens. 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