| Citation: | HUANG Jie, JIANG Zhiguo, ZHANG Haopeng, et al. Ship object detection in remote sensing images using convolutional neural networks[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(9): 1841-1848. doi: 10.13700/j.bh.1001-5965.2016.0755(in Chinese)
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Object detection in remote sensing images is mostly suffered from complex background and multiple interferences of environment. In this paper, a new method of ship detection is proposed, which combines convolutional neural networks (CNN) and support vector machine (SVM) to complete the ship detection task. Convolutional layers were adopted for feature extraction, taking advantages of independent feature extraction of CNNs and avoiding the process of complicated feature selection and extraction, which leads to better detection performance in complex background images. Meanwhile, since the samples of warship are difficult to acquire, samples of civil ship were employed as assistant samples for warship detection based on transfer learning theory. And this transfer learning method is proved to be effective by the experimental results, which performs better than the model trained only with warship samples. According to the parameter tuning and experimental validation, this method achieves a precision of 90.59% on testing dataset established by ourselves. In conclusion, this method possesses feasibility and robustness under different conditions of illumination and environment.
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