{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T03:09:41Z","timestamp":1773976181550,"version":"3.50.1"},"reference-count":62,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,10]],"date-time":"2022-02-10T00:00:00Z","timestamp":1644451200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National\u2002Natural\u2002Science\u2002Foundation\u2002of\u2002China","award":["61961035"],"award-info":[{"award-number":["61961035"]}]},{"name":"National Key R&amp;D Program of China","award":["2020YFD1001005"],"award-info":[{"award-number":["2020YFD1001005"]}]},{"name":"Science &amp; Technology Nova Program of Xinjiang Production and Construction Corps","award":["2018CB020"],"award-info":[{"award-number":["2018CB020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate cotton maps are crucial for monitoring cotton growth and precision management. The paper proposed a county-scale cotton mapping method by using random forest (RF) feature selection algorithm and classifier based on selecting multi-features, including spectral, vegetation indices, and texture features. The contribution of texture features to cotton classification accuracy was also explored in addition to spectral features and vegetation index. In addition, the optimal classification time, feature importance, and the best classifier on the cotton extraction accuracy were evaluated. The results showed that the texture feature named the gray level co-occurrence matrix (GLCM) is effective for improving classification accuracy, ranking second in contribution among all studied spectral, VI, and texture features. Among the three classifiers, the RF showed higher accuracy and better stability than support vector machines (SVM) and artificial neural networks (ANN). The average overall accuracy (OA) of the classification combining multiple features was 93.36%, 7.33% higher than the average OA of the single-time spectrum, and 2.05% higher than the average OA of the multi-time spectrum. The classification accuracy after feature selection by RF can still reach 92.12%, showing high accuracy and efficiency. Combining multiple features and random forest methods may be a promising county-scale cotton classification method.<\/jats:p>","DOI":"10.3390\/rs14040829","type":"journal-article","created":{"date-parts":[[2022,2,11]],"date-time":"2022-02-11T02:40:17Z","timestamp":1644547217000},"page":"829","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":79,"title":["Cotton Classification Method at the County Scale Based on Multi-Features and Random Forest Feature Selection Algorithm and Classifier"],"prefix":"10.3390","volume":"14","author":[{"given":"Hao","family":"Fei","sequence":"first","affiliation":[{"name":"School of Information Engineering, Tarim University, Alaer 843300, China"}]},{"given":"Zehua","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Tarim University, Alaer 843300, China"}]},{"given":"Chengkun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Tarim University, Alaer 843300, China"}]},{"given":"Nannan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Southern Xinjiang Research Center for Information Technology in Agriculture, Tarim University, Alaer 843300, China"},{"name":"College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China"}]},{"given":"Tao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Tarim University, Alaer 843300, China"},{"name":"Southern Xinjiang Research Center for Information Technology in Agriculture, Tarim University, Alaer 843300, China"}]},{"given":"Rengu","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Tarim University, Alaer 843300, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0095-558X","authenticated-orcid":false,"given":"Tiecheng","family":"Bai","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Tarim University, Alaer 843300, China"},{"name":"Southern Xinjiang Research Center for Information Technology in Agriculture, Tarim University, Alaer 843300, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,10]]},"reference":[{"key":"ref_1","unstructured":"Shengyong, M., and Zhicai, Y. 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