Video Satellite Imagery Super Resolution via Convolutional Neural Networks

Abstract

Video satellite imagery is a new technique for earth dynamic observation and has a wide range of uses in environmental fields. Despite its capability of dynamic targets' detection, it sustains a serious restriction of the image quality due to the degradation and compression in its imaging process. Hence, the super-resolution (SR) reconstruction on these compressed low-spatial-resolution images is of significance to afterward ground objects recognition and detection tasks. Based on the recent proposed state-of-the-art convolutional neural networks (CNNs) SR methods, we proposed an SR method which could get more precise reconstructed high-spatial-resolution images. Trained with Gaofen-2 satellite images, a robust CNN model specified in satellite image SR is obtained. Experimentally, the reconstruction results on Jilin-1 mission satellite images validate the effectiveness of our method.