Multiple Feature Analysis for Infrared Small Target Detection

Detection of small target has been an important and challenging task in infrared systems. Most detection algorithms which only use single metric are difficult to separate target from clutter completely. The false alarm may be high when there exists complex backgrounds. In this letter, multiple novel...

Full description

Saved in:
Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2017-08, Vol.14 (8), p.1333-1337
Main Authors: Bi, Yanguang, Bai, Xiangzhi, Jin, Ting, Guo, Sheng
Format: Article
Language:English
Subjects:
Alarm systems
Algorithms
Clutter
Detection
Extraterrestrial measurements
Feature extraction
Image edge detection
Information entropy
Infrared analysis
infrared background
learn-based classifier
Object detection
Segmentation
small target detection
Target detection
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Detection of small target has been an important and challenging task in infrared systems. Most detection algorithms which only use single metric are difficult to separate target from clutter completely. The false alarm may be high when there exists complex backgrounds. In this letter, multiple novel features are proposed from four aspects to establish elaborate description. Each feature reflects specific characteristic of small target. The best feature vector is selected to apply these features for detection. Then, learning-based classifier is trained to screen candidate targets which are obtained by initial segmentation. Experimental results demonstrate that the proposed features could discriminate small targets from various clutters effectively. The better detection performance is achieved compared with other methods in different infrared backgrounds.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2017.2711047