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Airport Detection Based on Improved Faster RCNN in Large Scale Remote Sensing Images

机译：基于大规模遥感图像中提高RCNN的机场检测

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摘要

The extensive acquisition and using of high-resolution remote sensing images have greatly promoted the development of airport detection. However, due to the complex shape, background and different scale of the airport location, the real-time and accuracy of airport detection are also facing major challenges. These factors can reduce the detection accuracy. To solve the above problems, we propose an improved faster region-based convolutional neural network (RCNN) detection method for airport detection in large scale remote sensing images. Multi-scale training is applied to faster RCNN to enhance the robustness of network for detecting airport with different sizes. Meanwhile, we adopt the modified multitask loss function to improve the accuracy of airport detection. Online hard example mining strategy is introduced to decrease the redundant negative samples in the training process. Then the non-maximum suppression method is used to remove the redundant boxes of the detected airport. Finally, we conduct sufficient experiments with the airport data obtained from Google Earth and make comparison with the state-of-the-art airport detection methods. The results show that the proposed method can accurately detect different airports under complex background with high detection rate, low false alarm rate and short running time.
机译：广泛的收购和使用高分辨率遥感图像极大地促进了机场检测的发展。然而，由于机场地点的复杂形状，背景和不同规模，机场检测的实时和准确性也面临着重大挑战。这些因素可以降低检测精度。为了解决上述问题，我们提出了一种改进的基于区域的卷积神经网络（RCNN）检测方法，用于在大规模遥感图像中的机场检测。多尺度培训应用于更快的RCNN，以增强网络检测机场的鲁棒性，以不同的尺寸。同时，我们采用改进的多任务损耗功能来提高机场检测的准确性。在线硬示例挖掘策略被引入训练过程中的冗余负样本。然后，非最大抑制方法用于删除检测到的机场的冗余盒子。最后，我们对从谷歌地球获得的机场数据进行了足够的实验，并与最先进的机场检测方法进行比较。结果表明，该方法可以在复杂的背景下准确地检测不同机场，具有高检测率，低误报率和运行时间短。

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