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dc.contributor.authorSansanee Auephanwiriyakulen_US
dc.contributor.authorYutthana Munklangen_US
dc.contributor.authorNipon Theera-Umponen_US
dc.description.abstract© Springer International Publishing Switzerland 2016. Synthetic aperture radar (SAR) image classification is one of the challenging problems because of the difficult characteristics of SAR images. In this chapter, we implement SAR image classification on three military vehicles types, i.e., T72 tank, BMP2 armored personnel carriers (APCs), and BTR70 APCs. The texture features generated from the fuzzy co-occurrence matrix (FCOM) are utilized with the multi-class support vector machine (MSVM) and the radial basis function (RBF) network. Finally, the ensemble average is implemented as a fusion tool as well. The best detection result is at 97.94% correct detection from the fusion of twenty best FCOM with RBF network models (ten best RBF network models at d = 5 and other ten best RBF network models at d = 10). Whereas the best fusion result of FCOM with MSVM is at 95.37% correct classification. This comes from the fusion of ten best MSVM models at d = 5 and other ten best MSVM models at d = 10. As a comparison we also generate features from the gray level co-occurrence matrix (GLCM). This feature set is implemented on the same classifiers. The results from FCOM are better than those from GLCM in all cases.en_US
dc.subjectComputer Scienceen_US
dc.titleSynthetic aperture radar (Sar) automatic target recognition (atr) using fuzzy co-occurrence matrix texture featuresen_US
dc.typeBook Seriesen_US
article.title.sourcetitleStudies in Computational Intelligenceen_US
article.volume621en_US Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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