Airplane Detection in Optical Remote Sensing Video Using Spatial and Temporal Features

Benefited from the rapid development of deep learning, object detection in natural image has made great improvements. However, since the size of the optical remote sensing video is very large while the size of airplane is very small, airplane detection in optical remote sensing video still faces a lot of challenges. In this article, we aim at a novel approach for airplane detection in optical remote sensing video. The proposed approach utilizes spatial features from structured forests edge detection and temporal features from neighboring frames. It is capable of circumventing existing challenges and running at a high speed for practical applications. To realize this goal, edge detection results of optical remote sensing video frames are obtained from structured forests edge detection method. Afterwards, improved frames differencing method is utilized to extract temporal features. Finally, airplane detection result is generated by deep neural networks with extracted spatial and temporal features. Our experiments demonstrate that our method has a great breakthrough on the precision and recall of airplane detection in optical remote sensing video.