A survey on Airport detection on remote sensing images using deep Convolutional Neural Network

Abstract

This survey 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 is mostly used in many applications 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 one of the classifier is adopted to detect airports. As per the research in various field 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 using Line Segment Detection algorithm and then uses these candidate regions to train CNN architecture with Matconv-net tool. 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.