Aircraft detection in remote sensing images using cascade convolutional neural networks

Abstract

Traditional aircraft detection algorithms which adopt handcraft features have poor performance in complex scene images and recognizing multi-scale objects. Methods using deep convolutional neural networks still face difficulty in dim small target search and recognition in large images with complex background. Aiming at these problems, a coarse-to-fine algorithm for aircraft detection in remote sensing images using cascade convolutional neural networks is proposed. To quickly and effectively acquire suspicious regions of interest (ROI), the whole image is searched by a small and shallow fully convolutional neural network which could deal with images of any size. Then deeper convolutional neural networks are used to refine the classification and location of the ROIs. A multilayer perceptron is introduced to the convolutional layer to improve identification capability of the convolutional neural networks and the strategies of multi-task learning and offline hard example mining are adopted in the process of training. At the detecting stage, the image pyramid is constructed and the redundant windows could be eliminated by the non-maximal suppression. Multiple datasets are tested and the results show that the proposed method has higher accuracy and stronger robustness and provides a fast and efficient solution for object detection in large remote sensing images.