NDLI: Salient target detection in remote sensing image via cellular automata

Content Provider	IEEE Xplore Digital Library
Author	Gang Wang Yongguang Chen Suochang Yang Min Gao Ganlin Shan
Copyright Year	2015
Description	Author affiliation: Shijiazhuang Mech. Eng. Coll., Shijiazhuang, China (Gang Wang; Suochang Yang; Min Gao; Ganlin Shan) \|\| Beijing Inst. of Tracking & Telecommun. Technol., Beijing, China (Yongguang Chen)
Abstract	In order to detect salient target in remote sensing images effectively and accurately, this paper propose a target segmentation method based on cellular automata which is usually used as a dynamic evolution model. First, we introduce the background based map to obtain saliency map with the help of a widely used superpixel segmentation method named simple linear iterative clustering. Secondly, cellular automata are employed to produce the elementary saliency map. Then enhanced saliency map can be obtained by maximum contrast of image patch method. Adaptive threshold is calculated to segment the enhanced saliency map. Consequently, the salient target detection and segmentation result can be obtained. Experiments on optical remote sensing images and synthetic aperture radar (SAR) images demonstrate that the proposed algorithm outperforms other methods such as K-means, Otsu and region growing method.
Starting Page	417
Ending Page	420
File Size	410271
Page Count	4
File Format	PDF
ISBN	9781479917150
e-ISBN	9781479917174
DOI	10.1109/ICICIP.2015.7388207
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Automata Object detection Optical imaging Adaptive optics Cellular automata Saliency map Remote sensing Synthetic aperture radar Target detection
Content Type	Text
Resource Type	Article

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