Remote Sensing Applications and Decision Support

Rule-based land cover classification from very high-resolution satellite image with multiresolution segmentation

[+] Author Affiliations
Md. Enamul Haque

Temple University, Department of Computer and Information Sciences, 1801 N, Broad Street, Philadelphia, Pennsylvania 19122, United States

Baqer Al-Ramadan

King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia

Brian A. Johnson

Institute for Global Environmental Strategies, 2108-11 Kamiyamaguchi, Hayama, Kanagawa, Japan

J. Appl. Remote Sens. 10(3), 036004 (Jul 11, 2016). doi:10.1117/1.JRS.10.036004
History: Received February 15, 2016; Accepted June 23, 2016
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Abstract.  Multiresolution segmentation and rule-based classification techniques are used to classify objects from very high-resolution satellite images of urban areas. Custom rules are developed using different spectral, geometric, and textural features with five scale parameters, which exploit varying classification accuracy. Principal component analysis is used to select the most important features out of a total of 207 different features. In particular, seven different object types are considered for classification. The overall classification accuracy achieved for the rule-based method is 95.55% and 98.95% for seven and five classes, respectively. Other classifiers that are not using rules perform at 84.17% and 97.3% accuracy for seven and five classes, respectively. The results exploit coarse segmentation for higher scale parameter and fine segmentation for lower scale parameter. The major contribution of this research is the development of rule sets and the identification of major features for satellite image classification where the rule sets are transferable and the parameters are tunable for different types of imagery. Additionally, the individual objectwise classification and principal component analysis help to identify the required object from an arbitrary number of objects within images given ground truth data for the training.

© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Md. Enamul Haque ; Baqer Al-Ramadan and Brian A. Johnson
"Rule-based land cover classification from very high-resolution satellite image with multiresolution segmentation", J. Appl. Remote Sens. 10(3), 036004 (Jul 11, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.036004


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