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https://hdl.handle.net/20.500.14365/2022
Title: | Collective Network of Binary Classifier Framework for Polarimetric Sar Image Classification: an Evolutionary Approach | Authors: | Kiranyaz, Serkan İnce, Türker Uhlmann, Stefan Gabbouj, Moncef |
Keywords: | Evolutionary classifiers multidimensional particle swarm optimization (MD-PSO) polarimetric synthetic aperture radar (SAR) Unsupervised Classification Automatic Classification Segmentation Decomposition |
Publisher: | IEEE-Inst Electrical Electronics Engineers Inc | Abstract: | Terrain classification over polarimetric synthetic aperture radar (SAR) images has been an active research field where several features and classifiers have been proposed up to date. However, some key questions, e.g., 1) how to select certain features so as to achieve highest discrimination over certain classes?, 2) how to combine them in the most effective way?, 3) which distance metric to apply?, 4) how to find the optimal classifier configuration for the classification problem in hand?, 5) how to scale/adapt the classifier if large number of classes/features are present?, and finally, 6) how to train the classifier efficiently to maximize the classification accuracy?, still remain unanswered. In this paper, we propose a collective network of (evolutionary) binary classifier (CNBC) framework to address all these problems and to achieve high classification performance. The CNBC framework adapts a Divide and Conquer type approach by allocating several NBCs to discriminate each class and performs evolutionary search to find the optimal BC in each NBC. In such an (incremental) evolution session, the CNBC body can further dynamically adapt itself with each new incoming class/feature set without a full-scale retraining or reconfiguration. Both visual and numerical performance evaluations of the proposed framework over two benchmark SAR images demonstrate its superiority and a significant performance gap against several major classifiers in this field. | URI: | https://doi.org/10.1109/TSMCB.2012.2187891 https://hdl.handle.net/20.500.14365/2022 |
ISSN: | 1083-4419 1941-0492 |
Appears in Collections: | PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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