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:: Volume 2, Issue 3 (12-2014) ::
jgit 2014, 2(3): 69-84 Back to browse issues page
The Use of Multiple Classifier Systems For Improving the Classification Accuracy of High–Dimensional Fully Polarimetric SAR Images
Iman Khosravi *, Akhondzadeh Mehdi
University of Tehran
Abstract:   (2549 Views)

A fully polarimetric synthetic aperture radar (POLSAR) image can provide a high-dimensional data. This large amount of information can increase the overall accuracy of land-cover classification. But increasing the data dimensions if inadequately number of training samples may increase the complexity and cause the curse of dimensionality phenomenon. One of the strategies for solving this problem is the use of multiple classifier systems (MCS) that has the capability of divide and conquer to the large data as compared to the individual classifiers. In addition, some of MCS methods using the weak and unstable classifiers such as decision tree (DT) and neural network (NN) can obtain the high accuracy in high-dimensional data. The objective of this paper is also to use several popular MCS methods such as adaboost, bagging and random forests in order to improve the accuracy of land-cover classification from high-dimensional PolSAR images. The data used in this paper are Radarsat-2 image from San Francisco Bay and AIRSAR image of Flevoland. For classifying two these images, 69 polarimetric features were extracted from them. Two classifiers of DT and NN were chosen as the base classifiers of adaboost and bagging methods. In the next, the MCS methods were compared with the individual classifiers of DT and NN. The results indicated the higher overall accuracy of MCS methods between 5%–8% for classifying first image and 9%–16% for classifying second image. Even, the producerchr('39')s accuracy and userchr('39')s accuracy of MCS methods at all classes were more than the those of individual classifiers. So that at some classes, the difference was between 20% to even near 50%. These results confirmed that the MCS methods not only can produce higher overall accuracy at land-cover classification, but also they have the higher efficiency and reliability at discriminate individual classes.

Keywords: multiple classifier system, fully polarimetric image, radar, SAR, high-dimensional
Full-Text [PDF 975 kb]   (771 Downloads)    
Type of Study: Research |
Received: 2015/11/22 | Accepted: 2015/11/22 | Published: 2015/11/22
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Khosravi I, Mehdi A. The Use of Multiple Classifier Systems For Improving the Classification Accuracy of High–Dimensional Fully Polarimetric SAR Images. jgit. 2014; 2 (3) :69-84
URL: http://jgit.kntu.ac.ir/article-1-162-en.html


Volume 2, Issue 3 (12-2014) Back to browse issues page
نشریه علمی-پژوهشی مهندسی فناوری اطلاعات مکانی Engineering Journal of Geospatial Information Technology
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