Superresolution approach of remote sensing images based on deep convolutional neural network

Abstract

Nowadays, remote sensing images have been widely used in civil and military fields. But, because of the limitations of the current imaging sensors and complex atmospheric conditions, the resolution of remote sensing images is often low. In this paper, a superresolution reconstruction algorithm based on the deep convolution neural network to improve the resolution of the remote sensing image is proposed. First, this algorithm learned a series of features of the mapping between high and low resolution images in the training phase. This mapping is expressed as a kind of deep convolutional neural network; the trained network is a series of parameter optimization for super-resolution reconstruction of remote sensing image. Experimental results show that the superresolution algorithm proposed in this paper can keep the details subjectively and improve the evaluation index objectively.