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30 April 2018 Comparison of SVM and CNN classification methods for infrared target recognition
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Recognizing targets from infrared images is a very important task for defense system. Recently, deep learning becomes an important solution of the classification problems which can be used for target recognition. In this study, a machine learning approach SVM and a deep learning approach CNN are compared for target recognition on infrared images. This paper applies SVM to measure the linear separability of the classes and obtain the baseline performance for the classes. Then, the constructed CNN model is applied to the dataset. The experimental results show that CNN model increases the overall performance around % 7.7 than SVM on prepared infrared image datasets.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ozan Yardimci and Barış Ç. Ayyıldız "Comparison of SVM and CNN classification methods for infrared target recognition", Proc. SPIE 10648, Automatic Target Recognition XXVIII, 1064804 (30 April 2018);

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