Machine learning approaches for high-resolution urban land cover classification: a comparative study

Authors:
Ranga Raju Vatsavai;Eddie Bright;Chandola Varun;Bhaduri Budhendra;Anil Cheriyadat;Jordan Grasser
Affiliations:
Oak Ridge National Laboratory, Oak Ridge, TN;Oak Ridge National Laboratory, Oak Ridge, TN;Oak Ridge National Laboratory, Oak Ridge, TN;Oak Ridge National Laboratory, Oak Ridge, TN;Oak Ridge National Laboratory, Oak Ridge, TN;Oak Ridge National Laboratory, Oak Ridge, TN
Venue:
Proceedings of the 2nd International Conference on Computing for Geospatial Research & Applications
Year:
2011

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Abstract

The proliferation of several machine learning approaches makes it difficult to identify a suitable classification technique for analyzing high-resolution remote sensing images. In this study, ten classification techniques were compared from five broad machine learning categories. Surprisingly, the performance of simple statistical classification schemes like maximum likelihood and Logistic regression over complex and recent techniques is very close. Given that these two classifiers require little input from the user, they should still be considered for most classification tasks. Multiple classifier systems is a good choice if the resources permit.