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Airplane Detection in Remote Sensing Images using Convolutional Neural Networks

机译:卷积神经网络在遥感影像中的飞机检测

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Airplane detection in remote sensing images remains a challenging problem and has also been taking a great interest to researchers. In this paper we propose an effective method to detect airplanes in remote sensing images using convolutional neural networks. Deep learning methods show greater advantages than the traditional methods with the rise of deep neural networks in target detection, and we give an explanation why this happens. To improve the performance on detection of airplane, we combine a region proposal algorithm with convolutional neural networks. And in the training phase, we divide the background into multi classes rather than one class, which can reduce false alarms. Our experimental results show that the proposed method is effective and robust in detecting airplane.
机译:遥感图像中的飞机检测仍然是一个具有挑战性的问题,并且也引起了研究人员的极大兴趣。在本文中,我们提出了一种使用卷积神经网络检测遥感图像中飞机的有效方法。随着深度神经网络在目标检测领域的兴起,深度学习方法比传统方法具有更大的优势,我们将解释为什么会发生这种情况。为了提高飞机的检测性能,我们将区域提议算法与卷积神经网络相结合。在训练阶段,我们将背景分为多个类别,而不是一个类别,这样可以减少错误警报。我们的实验结果表明,该方法在飞机检测中是有效且鲁棒的。

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