Remote Sensing Applications and Decision Support

Robust aircraft segmentation from very high-resolution images based on bottom-up and top-down cue integration

[+] Author Affiliations
Feng Gao

Beihang University, School of Computer Science and Engineering, Beijing Key Laboratory of Digital Media, 37 Xueyuan Road, Beijing 100191, China

Ocean University of China, Department of Computer Science and Technology, 238 Songling Road, Qingdao 266100, China

Qizhi Xu, Bo Li

Beihang University, School of Computer Science and Engineering, Beijing Key Laboratory of Digital Media, 37 Xueyuan Road, Beijing 100191, China

J. Appl. Remote Sens. 10(1), 016003 (Jan 19, 2016). doi:10.1117/1.JRS.10.016003
History: Received October 1, 2015; Accepted December 21, 2015
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Abstract.  Existing segmentation methods require manual interventions to optimally extract objects from cluttered background, so that they can hardly work well in automated surveillance systems. In order to automatically extract aircrafts from very high-resolution images, we proposed a segmentation method that combines bottom-up and top-down cues. Three essential principles from local contrast, global contrast, and center bias are involved to compute bottom-up cue. In addition, top-down cue is computed by incorporating aircraft shape priors, and it is achieved by training a classifier from a rich set of visual features. Iterative operations and adaptive fitting are designed to get refined results. Experimental results demonstrated that the proposed method can provide significant improvements on the segmentation accuracy.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Feng Gao ; Qizhi Xu and Bo Li
"Robust aircraft segmentation from very high-resolution images based on bottom-up and top-down cue integration", J. Appl. Remote Sens. 10(1), 016003 (Jan 19, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.016003


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