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14 December 2015 Unsupervised-learning airplane detection in remote sensing images
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Proceedings Volume 9815, MIPPR 2015: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 981503 (2015) https://doi.org/10.1117/12.2205827
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China
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.
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Wenjie Zhang, Wu Lv, Yifei Zhang, Jinwen Tian, and Jie Ma "Unsupervised-learning airplane detection in remote sensing images", Proc. SPIE 9815, MIPPR 2015: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 981503 (14 December 2015); https://doi.org/10.1117/12.2205827
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