Abstract
Due to the complicated maritime climate environment, the detection of marine Ship by using Remote sensing images is faced with many challenges in the field of object detection. In this paper, a ship detection method based on dark channel priority haze removal and Faster RCNN is proposed to solve this problem. We label and experiment with thousands of ships images on the sea. Compared with the using of object detection model directly and some traditional methods, the detection accuracy of the new method is obviously improved.
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Acknowledgements
This work was supported by National Key Research and Development Plan of China (2016YFC0803000, 2016YFB0502604), National Natural Science Fund of China (61472039), and Frontier and Interdisciplinary Innovation Program of Beijing Institute of Technology (2016CX11006), International Scientific and Technological Cooperation and Academic Exchange Program of Beijing Institute of Technology (GZ2016085103).
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Yuan, Z., Geng, J., Dai, T. (2018). Ship Detection from Remote Sensing Images Based on Deep Learning. In: Yuan, H., Geng, J., Liu, C., Bian, F., Surapunt, T. (eds) Geo-Spatial Knowledge and Intelligence. GSKI 2017. Communications in Computer and Information Science, vol 848. Springer, Singapore. https://doi.org/10.1007/978-981-13-0893-2_36
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DOI: https://doi.org/10.1007/978-981-13-0893-2_36
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