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Open AccessArticle

Land Cover Classification for Polarimetric SAR Images Based on Mixture Models

Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
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Remote Sens. 2014, 6(5), 3770-3790; https://doi.org/10.3390/rs6053770
Received: 7 February 2014 / Revised: 26 March 2014 / Accepted: 1 April 2014 / Published: 28 April 2014
In this paper, two mixture models are proposed for modeling heterogeneous regions in single-look and multi-look polarimetric SAR images, along with their corresponding maximum likelihood classifiers for land cover classification. The classical Gaussian and Wishart models are suitable for modeling scattering vectors and covariance matrices from homogeneous regions, while their performance deteriorates for regions that are heterogeneous. By comparison, the proposed mixture models reduce the modeling error by expressing the data distribution as a weighted sum of multiple component distributions. For single-look and multi-look polarimetric SAR data, complex Gaussian and complex Wishart components are adopted, respectively. Model parameters are determined by employing the expectation-maximization (EM) algorithm. Two maximum likelihood classifiers are then constructed based on the proposed mixture models. These classifiers are assessed using polarimetric SAR images from the RADARSAT-2 sensor of the Canadian Space Agency (CSA), the AIRSAR sensor of the Jet Propulsion Laboratory (JPL) and the EMISAR sensor of the Technical University of Denmark (DTU). Experiment results demonstrate that the new models fit heterogeneous regions preferably to the classical models and are especially appropriate for extremely heterogeneous regions, such as urban areas. The overall accuracy of land cover classification is also improved due to the more refined modeling. View Full-Text
Keywords: land cover classification; mixture model; polarimetric SAR land cover classification; mixture model; polarimetric SAR
MDPI and ACS Style

Gao, W.; Yang, J.; Ma, W. Land Cover Classification for Polarimetric SAR Images Based on Mixture Models. Remote Sens. 2014, 6, 3770-3790.

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