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.

Date Published: 8 November 2012
PDF: 12 pages
Proc. SPIE 8537, Image and Signal Processing for Remote Sensing XVIII, 85370G (8 November 2012); doi: 10.1117/12.973766

Published in SPIE Proceedings Vol. 8537:
Image and Signal Processing for Remote Sensing XVIII
Lorenzo Bruzzone, Editor(s)