Small target detection using an optimization-based filter
Kai Xie, Keren Fu, Tao Zhou, Jie Yang, Qiang Wu, Xiangjian He
Abstract
Small target detection is a critical problem in the Infrared Search And Track (IRST) system. Although it has been studied for years, there are some challenges remained, e.g. cloud edges and horizontal lines are likely to cause false alarms. This paper proposes a novel method using an optimization-based filter to detect infrared small target in heavy clutter. First, we design a certain pixel area as active area. Second, a weighted quadratic cost function is performed in the active area. Finally, a filter based on statistics of active area is derived from the cost function. Our method could preserve heterogeneous area, meanwhile, remove target region. Experimental results show our method achieves satisfied performance in heavy clutter.
BibTeX
@inproceedings{icassp2015_smalltargetdetec,
title = {Small target detection using an optimization-based filter},
author = {Kai Xie and Keren Fu and Tao Zhou and Jie Yang and Qiang Wu and Xiangjian He},
booktitle = {ICASSP 2015},
year = {2015}
}