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Proceedings Paper

Small infrared target detection algorithm based on mathematical morphology
Author(s): Feng Jiang; Min-hui Jin; Li-quan Song
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Paper Abstract

In infrared detection, we utilize the infrared radiation emitted from target to detect and identify the target we need as soon as possible, so we can get more reaction time for on the battlefield. However, because of the long distance between target and the sensor, the area of the image into target is very small. Infrared small target detection has become a difficult area of target recognition. In order to improve the accuracy of target detection,, we should preprocessing the infrared image before identifying target, including noise reduction, background suppression and target enhancement, after that, we can lay the foundation for the recognition processing. Firstly, we should filter the image by using median filter. Because the median filter not only filter noise effectively (especially the impulse noise) but also maintain the goal of good details, and the operation of this method is simple and faster. Secondly, we want to suppress the background. In describing the signal characteristics, mathematical morphology has unique advantages, so we suppress the image background in this way. Thirdly, after the above treatment, we can use adaptive threshold to achieve the objectives of the segmentation, and get the binary image obtained.

Paper Details

Date Published: 8 September 2011
PDF: 7 pages
Proc. SPIE 8193, International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications, 81934L (8 September 2011); doi: 10.1117/12.902266
Show Author Affiliations
Feng Jiang, China Aerospace Science & Industry Corp. (China)
Min-hui Jin, China Aerospace Science & Industry Corp. (China)
Li-quan Song, China Aerospace Science & Industry Corp. (China)


Published in SPIE Proceedings Vol. 8193:
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications
Jeffery J. Puschell; Junhao Chu; Haimei Gong; Jin Lu, Editor(s)

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