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• W2028607560 abstract "Various image classification methods have been developed for land cover mapping. Among them, classification trees and their new modifications, such as random forests (RF), have proven effective. However, these tree-based methods typically perform per-pixel classification, which often produces suboptimal results with scattered misclassifications. This paper recommends applying smoothing techniques to address the problem and combines post-hoc smoothing with RF for land cover classification using moderate resolution remote sensing imagery and ancillary data. RF is used to produce probability maps for each type of land cover, a smoothing technique is employed to smooth the probability maps and then a maximum probability rule is applied on the smoothed probability maps to generate a land cover map by assigning each pixel to the class with highest class probability. This method was applied to classify land cover in the Jiuzhaigou Nature Reserve in China using Landsat Thematic Mapper (TM) Images and topographic ..." @default.
• W2028607560 created "2016-06-24" @default.
• W2028607560 creator A5068568126 @default.
• W2028607560 date "2013-09-01" @default.
• W2028607560 modified "2023-09-23" @default.
• W2028607560 title "Land cover classification using moderate resolution satellite imagery and random forests with post-hoc smoothing" @default.
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• W2028607560 doi "https://doi.org/10.1080/14498596.2013.819600" @default.
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