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Journal of Applied Remote Sensing

Rule-based land cover classification from very high-resolution satellite image with multiresolution segmentation
Author(s): Md. Enamul Haque; Baqer Al-Ramadan; Brian A. Johnson
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Paper 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.

Paper Details

Date Published: 11 July 2016
PDF: 21 pages
J. Appl. Rem. Sens. 10(3) 036004 doi: 10.1117/1.JRS.10.036004
Published in: Journal of Applied Remote Sensing Volume 10, Issue 3
Show Author Affiliations
Md. Enamul Haque, Temple Univ. (United States)
Baqer Al-Ramadan, King Fahd Univ. of Petroleum & Minerals (Saudi Arabia)
Brian A. Johnson, Institute for Global Environmental Strategies (Japan)

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