Infrared ship target detection based on the combination of Bayesian theory and SVM

Jin Lin; Qinzhang Yu; Guibin Chen

doi:10.1117/12.2541939

14 February 2020 Infrared ship target detection based on the combination of Bayesian theory and SVM

Jin Lin, Qinzhang Yu, Guibin Chen

Proceedings Volume 11429, MIPPR 2019: Automatic Target Recognition and Navigation; 1142919 (2020) https://doi.org/10.1117/12.2541939
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

Abstract

Detecting ship targets distributed in infrared images of clouds, waves and other complex disturbances in an unknown complex background, and determining the true ship target in the case where the target and the false alarm are very similar are widely used and challenging tasks. This paper proposes an infrared ship targets detection algorithm based on Bayesian theory and SVM combination. Bayesian theory can be used to estimate the probability of partial unknowns with incomplete information. Bayesian formula can be used to correct the probability of occurrence, and finally we can use the expected value and the modified probability to make the optimal decision. At the same time, support vector machine (SVM) is a novel small sample learning method with solid theoretical foundation. It does not involve probability measures and laws of large numbers, so it is different from existing statistical methods. Firstly, the paper introduces the infrared ship target detection method based on Bayesian theory. Then, the image is post-processed to remove redundant false alarm targets. Finally, the paper introduces the experimental data and the performance evaluation indicators of ship detection results, and compares with other ship detection methods to obtain experimental results.

Citation Download Citation

Jin Lin, Qinzhang Yu, and Guibin Chen "Infrared ship target detection based on the combination of Bayesian theory and SVM", Proc. SPIE 11429, MIPPR 2019: Automatic Target Recognition and Navigation, 1142919 (14 February 2020); https://doi.org/10.1117/12.2541939

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KEYWORDS

Target detection

Infrared radiation

Infrared imaging

Detection and tracking algorithms

Infrared detectors

Probability theory

Clouds

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