{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T16:22:35Z","timestamp":1771950155467,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2017,11,22]],"date-time":"2017-11-22T00:00:00Z","timestamp":1511308800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Statistical Science Research Project","award":["2016LY76"],"award-info":[{"award-number":["2016LY76"]}]},{"name":"the China Postdoctoral Science Foundation","award":["2015M572061"],"award-info":[{"award-number":["2015M572061"]}]},{"name":"the General Program of Shandong Natural Science Foundation","award":["ZR2015FM014"],"award-info":[{"award-number":["ZR2015FM014"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Multi-feature, especially multi-temporal, remote-sensing data have the potential to improve land cover classification accuracy. However, sometimes it is difficult to utilize all the features efficiently. To enhance classification performance based on multi-feature imagery, an improved rotation forest, combining Principal Component Analysis (PCA) and a boosting na\u00efve Bayesian tree (NBTree), is proposed. First, feature extraction was carried out with PCA. The feature set was randomly split into several disjoint subsets; then, PCA was applied to each subset, and new training data for linear extracted features based on original training data were obtained. These steps were repeated several times. Second, based on the new training data, a boosting na\u00efve Bayesian tree was constructed as the base classifier, which aims to achieve lower prediction error than a decision tree in the original rotation forest. At the classification phase, the improved rotation forest has two-layer voting. It first obtains several predictions through weighted voting in a boosting na\u00efve Bayesian tree; then, the first-layer vote predicts by majority to obtain the final result. To examine the classification performance, the improved rotation forest was applied to multi-feature remote-sensing images, including MODIS Enhanced Vegetation Index (EVI) imagery time series, MODIS Surface Reflectance products and ancillary data in Shandong Province for 2013. The EVI imagery time series was preprocessed using harmonic analysis of time series (HANTS) to reduce the noise effects. The overall accuracy of the final classification result was 89.17%, and the Kappa coefficient was 0.71, which outperforms the original rotation forest and other classifier ensemble results, as well as the NASA land cover product. However, this new algorithm requires more computational time, meaning the efficiency needs to be further improved. Generally, the improved rotation forest has a potential advantage in remote-sensing classification.<\/jats:p>","DOI":"10.3390\/rs9111205","type":"journal-article","created":{"date-parts":[[2017,11,22]],"date-time":"2017-11-22T10:47:38Z","timestamp":1511347658000},"page":"1205","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["An Improved Rotation Forest for Multi-Feature Remote-Sensing Imagery Classification"],"prefix":"10.3390","volume":"9","author":[{"given":"Yingchang","family":"Xiu","sequence":"first","affiliation":[{"name":"College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China"}]},{"given":"Wenbao","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China"}]},{"given":"Wenjing","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,11,22]]},"reference":[{"key":"ref_1","first-page":"2618","article-title":"A review of classification methods of remote sensing imagery","volume":"31","author":"Jia","year":"2011","journal-title":"Spectrosc. 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