Hyperspectral Image Super-resolution Using Generative Adversarial Network and Residual Learning

Abstract

Due to the limitation of image acquisition, hyperspectral remote sensing imagery is hard to reflect in both high spatial and spectral resolutions. Super-resolution (SR) is a technique which can improve the spatial resolution. Inspired by recent achievements in deep convolutional neural network (CNN) and generative adversarial network (GAN), a GAN based framework is proposed for hyperspectral image super-resolution. In the proposed method, residual learning is used to obtain a high metrics and spectral fidelity, and a shorter connection is set between the input layer and output layer. The gradient features from low-resolution (LR) image to high-resolution (HR) are utilized as auxiliary information to assist deep CNN to carry out counter training with discriminator. Experimental results demonstrate that the proposed SR algorithm achieves superior performance in spectral fidelity and spatial resolution compared with baseline methods.