8 March 2018 Airplane detection based on fusion framework by combining saliency model with Deep Convolutional Neural Networks
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Proceedings Volume 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 1061106 (2018) https://doi.org/10.1117/12.2283203
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China
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.
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Hao Dou, Xiao Sun, Bin Li, Qianqian Deng, Xubo Yang, Di Liu, Jinwen Tian, "Airplane detection based on fusion framework by combining saliency model with Deep Convolutional Neural Networks", Proc. SPIE 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 1061106 (8 March 2018); doi: 10.1117/12.2283203; https://doi.org/10.1117/12.2283203
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