TY - GEN
T1 - Machine learning approaches for high-resolution urban land cover classification
T2 - 2nd International Conference on Computing for Geospatial Research and Applications, COM.Geo 2011
AU - Vatsavai, Ranga Raju
AU - Varun, Chandola
AU - Cheriyadat, Anil
AU - Bright, Eddie
AU - Budhendra, Bhaduri
AU - Grasser, Jordan
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
KW - Bayesian classification
KW - MCS
KW - Neural networks
KW - Trees
UR - http://www.scopus.com/inward/record.url?scp=79960089324&partnerID=8YFLogxK
U2 - 10.1145/1999320.1999331
DO - 10.1145/1999320.1999331
M3 - Conference contribution
AN - SCOPUS:79960089324
SN - 9781450306812
T3 - ACM International Conference Proceeding Series
BT - COM.Geo 2011 - 2nd International Conference on Computing for Geospatial Research and Applications
Y2 - 23 May 2011 through 25 May 2011
ER -
