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Multispectral Image Analysis Using Random Forest
Computer Science Faculty Publications and Presentations
  • Barrett Lowe
  • Arun Kulkarni, University of Texas at Tyler
Classical methods for classification of pixels in multispectral images include supervised classifiers such as the maximum-likelihood classifier, neural network classifiers, fuzzy neural networks, support vector machines, and decision trees. Recently, there has been an increase of interest in ensemble learning – a method that generates many classifiers and aggregates their results. Breiman proposed Random Forestin 2001 for classification and clustering. Random Forest grows many decision trees for classification. To classify a new object, the input vector is run through each decision tree in the forest. Each tree gives a classification. The forest chooses the classification having the most votes. Random Forest provides a robust algorithm for classifying large datasets. The potential of Random Forest is not been explored in analyzing multispectral satellite images. To evaluate the performance of Random Forest, we classified multispectral images using various classifiers such as the maximum likelihood classifier, neural network, support vector machine (SVM), and Random Forest and compare their results.
International Journal of Soft Computing
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Barrett Lowe and Kulkarni A. D. (2015). Multispectral Image Analysis Using Random Forest, International Journal on Soft Computing, vol. 6, no. 2, pp 1-14
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Barrett Lowe and Arun Kulkarni. "Multispectral Image Analysis Using Random Forest" (2015)
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