Super-resolution reconstruction of remote sensing images based on convolutional neural network

Abstract

A method of super-resolution reconstruction of remote sensing images based on convolutional neural network is proposed to address the problems of low-resolution and poor visual quality of remote sensing images. In this method, a sample database with high-resolution (HR) and low-resolution (LR) remote sensing images was constructed. A convolutional neural network was then obtained by determining the intrinsic relationship between HR and LR remote sensing images in the sample database. Multiple pairs of HR and LR images were selected from the sample database and sent into a six-layer convolutional neural network. The experimental results showed that compared with other learning-based methods, such as the fast super-resolution convolutional neural network (FSRCNN), the image quality obtained by our method is improved both subjectively and objectively. Moreover, the training time was ∼17 % less than in the FSRCNN method.