NDLI: An unsupervised target detection algorithm in SAR images

Content Provider	IEEE Xplore Digital Library
Author	Lanying Cao
Copyright Year	2007
Description	Author affiliation: Chinese Leihua Electron. Technol. Res. Inst., Wuxi (Lanying Cao)
Abstract	An unsupervised target detection algorithm in SAR image is proposed in this paper. In the algorithm, 2-D fussy entropy is used as objective function. Ant colony algorithm and genetic algorithm are used to search the optimal thresholds for SAR target detection problem separately. In the ant colony algorithm, the ant move direction is determined by the trail pheromone. Each ant in the colony will generate a path based on the relative positions of the nodes and feedback information about the best paths generated by previous colonies. In the Genetic algorithm, the near optimal results are searched from selection, cross and mutation. Tests results showed that, due to ant colony algorithm's ability of both finding good search paths and escaping from local minima, the proposed method could achieve more stable target detection results than genetic algorithm though its detect performance was similar to that of genetic algorithm.
Starting Page	529
Ending Page	532
File Size	133930
Page Count	4
File Format	PDF
ISBN	9781424411870
DOI	10.1109/APSAR.2007.4418666
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-11-05
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image edge detection Genetic mutations Feedback Radar detection Object detection Entropy Synthetic aperture radar Pixel Genetic algorithms Testing
Content Type	Text
Resource Type	Article

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