TY - JOUR ID - 73125 TI - Optimum Ensemble Classification for Fully Polarimetric SAR Data Using Global-Local Classification Approach JO - International Journal of Engineering JA - IJE LA - en SN - 1025-2495 AU - Saleh, R AU - Farsi, H AD - Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran Y1 - 2018 PY - 2018 VL - 31 IS - 2 SP - 331 EP - 338 KW - PolSAR data KW - Ensemble classification KW - Global KW - local classification KW - H/α classifier KW - Clustering KW - Multi objective Optimization KW - Reliability DO - N2 - In this paper, a proposed ensemble classification for fully polarimetric synthetic aperture radar (PolSAR) data using a global-local classification approach is presented. In the first step, to perform the global classification, the training feature space is divided into a specified number of clusters. In the next step to carry out the local classification over each of these clusters, which contains elements of several classes, a base classifier is trained. Thus, an ensemble of classifiers has been formed which each of them acts professionally in a part of the feature space. To achieve more diversity, the data set is independently partitioned into variable number of clusters by  classifier and K-means algorithm. To combine outputs of different arrangements, majority voting, Naïve Bayes and a heuristic combination rule with taking into account the classification accuracy and reliability (which in PolSAR classification less attention has been paid to it) as objective functions, are used. The experimental results over two PolSAR images prove effectiveness of the proposed algorithms in comparison to the baseline methods. UR - https://www.ije.ir/article_73125.html L1 - https://www.ije.ir/article_73125_071b7337fbfd52421139065ef7891252.pdf ER -