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Proceedings Paper

Infrared ship target detection based on the combination of Bayesian theory and SVM
Author(s): Jin Lin; Qinzhang Yu; Guibin Chen
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Paper 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.

Paper Details

Date Published: 14 February 2020
PDF: 8 pages
Proc. SPIE 11429, MIPPR 2019: Automatic Target Recognition and Navigation, 1142919 (14 February 2020); doi: 10.1117/12.2541939
Show Author Affiliations
Jin Lin, Academy of Military Science (China)
Qinzhang Yu, People's Liberation Army (China)
Guibin Chen, Tsinghua Univ. (China)

Published in SPIE Proceedings Vol. 11429:
MIPPR 2019: Automatic Target Recognition and Navigation
Jianguo Liu; Hanyu Hong; Xia Hua, Editor(s)

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