Dim target detection method based on salient graph fusion

Ruo-lan Hu; Yi-yan Shen; Jun Jiang

doi:10.1117/12.2284940

19 February 2018 Dim target detection method based on salient graph fusion

Ruo-lan Hu, Yi-yan Shen, Jun Jiang

Proceedings Volume 10608, MIPPR 2017: Automatic Target Recognition and Navigation; 1060809 (2018) https://doi.org/10.1117/12.2284940
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China

Abstract

Dim target detection is one key problem in digital image processing field. With development of multi-spectrum imaging sensor, it becomes a trend to improve the performance of dim target detection by fusing the information from different spectral images. In this paper, one dim target detection method based on salient graph fusion was proposed. In the method, Gabor filter with multi-direction and contrast filter with multi-scale were combined to construct salient graph from digital image. And then, the maximum salience fusion strategy was designed to fuse the salient graph from different spectral images. Top-hat filter was used to detect dim target from the fusion salient graph. Experimental results show that proposal method improved the probability of target detection and reduced the probability of false alarm on clutter background images.

Citation Download Citation

Ruo-lan Hu, Yi-yan Shen, and Jun Jiang "Dim target detection method based on salient graph fusion", Proc. SPIE 10608, MIPPR 2017: Automatic Target Recognition and Navigation, 1060809 (19 February 2018); https://doi.org/10.1117/12.2284940

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available