An infrared small target detection algorithm based on high-speed local contrast method

作者：Cui, Zheng; Yang, Jingli^*; Jiang, Shouda; Li, Junbao

来源：Infrared Physics & Technology, 2016, 76: 474-481.

DOI：10.1016/j.infrared.2016.03.023

• Small target detection
• Human Visual System
• High speed local contrast method
• Machine learning

Small-target detection in infrared imagery with a complex background is always an important task in remote sensing fields. It is important to improve the detection capabilities such as detection rate, false alarm rate, and speed. However, current algorithms usually improve one or two of the detection capabilities while sacrificing the other. In this letter, an Infrared (IR) small target detection algorithm with two layers inspired by Human Visual System (HVS) is proposed to balance those detection capabilities. The first layer uses high speed simplified local contrast method to select significant information. And the second layer uses machine learning classifier to separate targets from background clutters. Experimental results show the proposed algorithm pursue good performance in detection rate, false alarm rate and speed simultaneously.

• 出版日期2016-5
• 单位哈尔滨工业大学