Comparison of SVM and CNN classification methods for infrared target recognition

Ozan Yardimci; Barış Ç. Ayyıldız

doi:10.1117/12.2303504

30 April 2018 Comparison of SVM and CNN classification methods for infrared target recognition

Ozan Yardimci, Barış Ç. Ayyıldız

Proceedings Volume 10648, Automatic Target Recognition XXVIII; 1064804 (2018) https://doi.org/10.1117/12.2303504
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

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.

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Citation Download Citation

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); https://doi.org/10.1117/12.2303504

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