November 2011

Conference Paper

Machine Learning Approaches for High-resolution Urban Land Cover Classification: A Comparative Study

By:
Vatsavai, Ranga R; Chandola, Varun ; Cheriyadat, Anil M; Bright, Edward A; Bhaduri, Budhendra L; Graesser, Jordan B
Page Number:
1-11
Book Title:
Proceedings of the 2nd International Conference and Exhibition on Computing for Geospatial Research & Application
Publication Date:
November 2011
Publisher Location:
ACM, New York, New Jersey, United States of America
Conference Name:
2nd International Conference on Computing for Geospatial Research and Applications
Conference Location:
Washington DC, District of Columbia, United States of America
Conference Sponsor:
HP, ACM

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.

Related Researchers