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Published in:
2017 | OriginalPaper | Chapter
Semi-Supervised Classification Based on SAGA for PolSAR Images
Authors : Hongying Liu, Zhi Wang, Feixiang Wang, Haisheng Deng, Licheng Jiao
Published in: Bio-inspired Computing: Theories and Applications
Publisher: Springer Singapore
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Abstract
Polarimetric Synthetic Aperture Radar (PolSAR) has been meeting the requirements in acquiring images for all-day, free of light, weather and other reasons, so it is widely applied in military and civilian life. PolSAR images contain abundant information. Its processing and interpretation have played more and more important role in national defense construction and economic development. However, the classification accuracy for PolSAR images using conventional clustering-based methods is quite limited. In this paper, a novel semi-supervised classification method is proposed. The Simulated Annealing-Genetic Algorithm (SAGA) is designed to optimize the iterative mechanism for finding the optimal centers of Fuzzy C-means (FCM) clustering, which avoids the local optimum. This leads to more accurate divisions on each category. Experimental results on synthesized and real PolSAR images confirm the superior performance of the proposed algorithm compared with conventional methods.