文章基本信息

标题：Multispectral Image Analysis Using Random Forest
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作者：Barrett Lowe ; Arun Kulkarni
期刊名称：International Journal on Soft Computing
电子版ISSN：2229-7103
出版年度：2015
卷号：6
期号：1
页码：1
DOI：10.5121/ijsc.2015.6101
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Classical methods for classification of pixels in multispectral images include supervised classifiers such asthe maximum-likelihood classifier, neural network classifiers, fuzzy neural networks, support vectormachines, and decision trees. Recently, there has been an increase of interest in ensemble learning – amethod that generates many classifiers and aggregates their results. Breiman proposed Random Forestin2001 for classification and clustering. Random Forest grows many decision trees for classification. Toclassify a new object, the input vector is run through each decision tree in the forest. Each tree gives aclassification. The forest chooses the classification having the most votes. Random Forest provides a robustalgorithm for classifying large datasets. The potential of Random Forest is not been explored in analyzingmultispectral satellite images. To evaluate the performance of Random Forest, we classified multispectralimages using various classifiers such as the maximum likelihood classifier, neural network, support vectormachine (SVM), and Random Forest and compare their results.
关键词：Classification; Decision Trees; Random Forest; Multispectral Images