8 November 2012 Target attribute-based false alarm rejection in small infrared target detection
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Abstract
Infrared search and track is an important research area in military applications. Although there are a lot of works on small infrared target detection methods, we cannot apply them in real field due to high false alarm rate caused by clutters. This paper presents a novel target attribute extraction and machine learning-based target discrimination method. Eight kinds of target features are extracted and analyzed statistically. Learning-based classifiers such as SVM and Adaboost are developed and compared with conventional classifiers for real infrared images. In addition, the generalization capability is also inspected for various infrared clutters.
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Sungho Kim, "Target attribute-based false alarm rejection in small infrared target detection", Proc. SPIE 8537, Image and Signal Processing for Remote Sensing XVIII, 85370G (8 November 2012); doi: 10.1117/12.973766; https://doi.org/10.1117/12.973766
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