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Airplane detection based on fusion framework by combining saliency model with Deep Convolutional Neural Networks
Author(s): Hao Dou; Xiao Sun; Bin Li; Qianqian Deng; Xubo Yang; Di Liu; Jinwen Tian
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Paper Abstract

Aircraft detection from very high resolution remote sensing images, has gained more increasing interest in recent years due to the successful civil and military applications. However, several problems still exist: 1) how to extract the high-level features of aircraft; 2) locating objects within such a large image is difficult and time consuming; 3) A common problem of multiple resolutions of satellite images still exists. In this paper, inspirited by biological visual mechanism, the fusion detection framework is proposed, which fusing the top-down visual mechanism (deep CNN model) and bottom-up visual mechanism (GBVS) to detect aircraft. Besides, we use multi-scale training method for deep CNN model to solve the problem of multiple resolutions. Experimental results demonstrate that our method can achieve a better detection result than the other methods.

Paper Details

Date Published: 8 March 2018
PDF: 8 pages
Proc. SPIE 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 1061106 (8 March 2018); doi: 10.1117/12.2283203
Show Author Affiliations
Hao Dou, Huazhong Univ. of Science and Technology (China)
Xiao Sun, China Academy of Launch Vehicle Technology Beijing (China)
Bin Li, China Academy of Electronics and Information Technology (China)
Qianqian Deng, Huazhong Univ. of Science and Technology (China)
Xubo Yang, Huazhong Univ. of Science and Technology (China)
Di Liu, Huazhong Univ. of Science and Technology (China)
Jinwen Tian, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 10611:
MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications
Nong Sang; Jie Ma; Zhong Chen, Editor(s)

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