Fast and accurate airport detection in remote sensing images is important for many military and civilian applications. However, traditional airport detection methods have low detection rates, high false alarm rates and slow speeds. Due to the power convolutional neural networks in object-detection systems, an end-to-end airport detection method based on convolutional neural networks is proposed in this study. First, based on the common low-level visual features of natural images and airport remote sensing images, region-based convolutional neural networks are chosen to conduct transfer learning for airport images using a limited amount of data. Second, to further improve the detection rate and reduce the false alarm rate, the concepts of "divide and conquer" and "integral loss" are introduced to establish cascade region proposal networks and multi-threshold detection networks, respectively. Third, hard example mining is used to improve the object discrimination ability and the training efficiency of the network during sample training. Additionally, a cross-optimization strategy is employed to achieve convolution layer sharing between the cascade region proposal networks and the subsequent multi-threshold detection networks, and this approach significantly decreases the detection time. The results show that the method established in this study can accurately detect various types of airports in complex backgrounds with a higher detection rate, lower false alarm rate, and shorter detection time than existing airport detection methods.
To address the issues encountered when using traditional airplane detection methods, including the low accuracy rate, high false alarm rate, and low detection speed due to small object sizes in aerial remote sensing images, we propose a remote sensing image airplane detection method that uses multilayer feature fusion in fully convolutional neural networks. The shallow layer and deep layer features are fused at the same scale after sampling to overcome the problems of low dimensionality in the deep layer and the inadequate expression of small objects. The sizes of candidate regions are modified to fit the size of the actual airplanes in the remote sensing images. The fully connected layers are replaced with convolutional layers to reduce the network parameters and adapt to different input image sizes. The region proposal network shares convolutional layers with the detection network, which ensures high detection efficiency. The simulation results indicate that, when compared to typical airplane detection methods, the proposed method is more accurate and has a lower false alarm rate. Additionally, the detection speed is considerably faster and the method can accurately and rapidly complete airplane detection tasks in aerial remote sensing images.
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