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

Conference · Fri Dec 31 23:00:00 EST 2010

OSTI ID:1029948

Vatsavai, Raju ^[1]; Chandola, Varun ^[1]; Cheriyadat, Anil M ^[1]; Bright, Eddie A ^[1]; Bhaduri, Budhendra L ^[1]; Graesser, Jordan B ^[1]

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1. ORNL

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.

• Research Organization: Oak Ridge National Laboratory (ORNL)
• Sponsoring Organization: ORNL work for others
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1029948
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
ALGORITHMS
ARTIFICIAL INTELLIGENCE
CLASSIFICATION
COMPARATIVE EVALUATIONS
DECISION TREE ANALYSIS
IMAGE PROCESSING
IMAGES
MAXIMUM-LIKELIHOOD FIT
NEURAL NETWORKS
PERFORMANCE
REGRESSION ANALYSIS
REMOTE SENSING
bayesian classifiers
decision trees
multiple classifier systems
neural networks