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Integrating object-oriented image analysis and decision tree algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR imagery

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4 Author(s)
Zhixin Qi ; Department of Urban Planning and Design, The University of Hong Kong, Pokfulam Road, China ; Anthony Gar-On Yeh ; Xia Li ; Zheng Lin

Traditional pixel-based classification methods yield poor results when applied to SAR imagery because of the presence of speckle and limited information in backscatter coefficients. A novel classification method, integrating polarimetric target decomposition, object-oriented image analysis, and decision tree algorithms, is proposed for the classification of polarimetric SAR data (PolSAR). The polarimetric target decomposition is aimed at extracting physical information related to the scattering mechanism of targets for the classification of scattering data. The main purposes of the object-oriented image analysis are delineating objects and extracting various spatial and textural features. The decision tree algorithm provides an efficient way to select features and create a decision tree for the classification. A comparison between the proposed method and the Wishart supervised classification was made. The overall accuracies of these two methods were 89.34% and 79.36%, respectively. The results show that the proposed method is an effective method for the classification of PolSAR data.

Published in:

Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International

Date of Conference:

25-30 July 2010