Deep convolutional neural networks for airport detection in remote sensing images

This study investigated the use of deep convolutional neural networks (CNNs) in providing a solution for the problem of airport detection in remote sensing images (RSIs). In recent years, Deep CNNs have gained much attention with numerous applications having been undertaken in the area of computer vision. Researchers generally approach airport detection as a pattern recognition problem, in which first various distinctive features are extracted, and then a classifier is adopted to detect airports. CNNs not only ensure a tuned feature vector, but also yield better classification accuracy. The method proposed in this study first detects various regions on RSIs and then uses these candidate regions to train CNN architecture. The CNN model used has five convolution and three fully connected layers. Normalization and dropout layers were employed in order to build efficient architecture. A data augmentation strategy was used to reduce overfitting. Several experiments were performed to evaluate the performance of CNNs. Comparative work validated the efficiency of the proposed method and yielded an accuracy of 95.21%.