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High resolution remote sensing image ship target detection technology based on deep learning

王敏  陈金勇  王港  高峰  孙康  许妙忠  
【摘要】:With the development of China's high-resolution special projects and the rapid development of commercial satellite, the resolution of the mainstream satellite remote sensing images has reached the sub-meter level. Ship target detection in high-resolution remote sensing images has always been the focus and hotspot in image understanding. Real-time and effective detection of ships play an extremely important role in marine transportation, military operations and so on. Firstly, the full-factor ship target sample library of high-resolution image is synthetically prepared. Then, based on the Faster R-CNN framework and Resnet model, optimize the parameters of the model to achieve accurate results. The simulation results show that the detection model trained in this paper has the highest recall rate of 98.01% and false alarm rate of 0.83%. It can be applied to the practical application of ship detection in remote sensing images.

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