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The framework is composed of five parts:<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><mml:mo stretchy=\"false\">(<\/mml:mo><mml:mn fontstyle=\"italic\">1<\/mml:mn><mml:mo stretchy=\"false\">)<\/mml:mo><\/mml:math>random samples selection with<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\"><mml:mo stretchy=\"false\">(<\/mml:mo><mml:mn fontstyle=\"italic\">2<\/mml:mn><mml:mo stretchy=\"false\">)<\/mml:mo><\/mml:math>probabilistic output initial random forest classification processing based on the number of votes;<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M3\"><mml:mo stretchy=\"false\">(<\/mml:mo><mml:mn fontstyle=\"italic\">3<\/mml:mn><mml:mo stretchy=\"false\">)<\/mml:mo><\/mml:math>semisupervised classification, which is an improvement of the supervision classification of random forest based on the weighted entropy algorithm;<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M4\"><mml:mo stretchy=\"false\">(<\/mml:mo><mml:mn fontstyle=\"italic\">4<\/mml:mn><mml:mo stretchy=\"false\">)<\/mml:mo><\/mml:math>precision evaluation; and<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M5\"><mml:mo stretchy=\"false\">(<\/mml:mo><mml:mn fontstyle=\"italic\">5<\/mml:mn><mml:mo stretchy=\"false\">)<\/mml:mo><\/mml:math>a comparison with the traditional minimum distance classification and the support vector machine (SVM) classification. In order to verify the universality of the proposed algorithm, two different data sources are tested, which are AVIRIS and Hyperion data. The results show that the overall classification accuracy of AVIRIS data is up to 87.36%, the kappa coefficient is up to 0.8591, and the classification time is 22.72s. Hyperion data is up to 99.17%, the kappa coefficient is up to 0.9904, and the classification time is 8.16s. Classification accuracy is obviously improved and efficiency is greatly improved, compared with the minimum distance and the SVM classifier and the CART classifier.<\/jats:p>","DOI":"10.1155\/2018\/3521720","type":"journal-article","created":{"date-parts":[[2018,9,4]],"date-time":"2018-09-04T19:30:49Z","timestamp":1536089449000},"page":"1-27","source":"Crossref","is-referenced-by-count":0,"title":["A New Semisupervised-Entropy Framework of Hyperspectral Image Classification Based on Random Forest"],"prefix":"10.1155","volume":"2018","author":[{"given":"Mengmeng","family":"Sun","sequence":"first","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7921-1016","authenticated-orcid":true,"given":"Chunyang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information 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