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Proceedings Paper

Unsupervised-learning airplane detection in remote sensing images
Author(s): Wenjie Zhang; Wu Lv; Yifei Zhang; Jinwen Tian; Jie Ma
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Paper Abstract

This paper attempts to develop an unsupervised learning approach for airplane detection in remote sensing images. This novel airplane detection method is based on circle-frequency filter and cluster-based co-saliency detection. Firstly, the CF-filter method is utilized as the coarse detection to detect target airplanes with some false alarms. Then, we collect all the detected targets and use cluster-based co-saliency detection to enhance the real airplanes and weaken the false alarms, so that most of the false alarms can be eliminated. Experimental results on real remote sensing images demonstrate the efficiency and accuracy of the proposed method.

Paper Details

Date Published: 14 December 2015
PDF: 6 pages
Proc. SPIE 9815, MIPPR 2015: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 981503 (14 December 2015); doi: 10.1117/12.2205827
Show Author Affiliations
Wenjie Zhang, Huazhong Univ. of Science and Technology (China)
Wu Lv, China State Shipbuilding System Engineering Research Institute (China)
Yifei Zhang, Huazhong Univ. of Science and Technology (China)
Jinwen Tian, Huazhong Univ. of Science and Technology (China)
Jie Ma, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 9815:
MIPPR 2015: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications
Jianguo Liu; Hong Sun, Editor(s)

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