By Topic

Improving the Accuracy of Urban Land Cover Classification Using Radarsat-2 PolSAR Data

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Maryam Salehi ; Dept. of Geomatics & Geodesy, K.N. Toosi Univ. of Technol., Tehran, Iran ; Mahmod Reza Sahebi ; Yasser Maghsoudi

Land cover classification is one of the most important applications of polarimetric SAR images, especially in urban areas. There are numerous features that can be extracted from these images, hence feature selection plays an important role in PolSAR image classification. In this study, three main steps are used to address this task: (1) feature extraction in the form of three categories, namely original data features, decomposition features, and SAR discriminators; (2) feature selection in the framework of the single and multi-objective optimization; and (3) image classification using the best subset of features. In single objective methods, we employ genetic algorithms (GAs) and support vector machines (SVMs) or multi-layer perceptron (MLP) neural network in order to maximize classification accuracy. Then a new method is proposed to perform an efficient land cover classification of the San Francisco Bay urban area based on the multi-objective optimization approach. The objectives are to minimize the error of classification and the number of selected PolSAR parameters. The experimental results on Radarsat-2 fine-quad data show that the proposed method outperforms the single objective approaches tested against it, while saving computational complexity. Finally, we show that the our method has a better performance than the SVM with full set of features and the Wishart classifier which is based on the covariance matrix.

Published in:

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  (Volume:7 ,  Issue: 4 )