Remote Sensing Image Synthesis via Graphical Generative Adversarial Networks

Abstract

We explore the use of graphical generative adversarial networks (Graphical-GAN) for synthesizing remote sensing images. The model is probabilistic graphical based generative adversarial networks (GAN). It pairs a generative network G with a recognition network R. Both of them are adversarially trained with a discriminative network D. Particularly, R is employed to infer the underlying causal relationships among both observed and latent variables from real remote sensing images. The advantages of the Graphical-GAN for synthesizing multiple categories of remote sensing images are two fold. Firstly, it considers the underlying causal relationships and captures the true data distribution of remote sensing images. Secondly, the adversarial learning generates synthetic sensing images that are similar to real ones with slight differences. Our remote sensing image synthesis scheme paves a promising way for remote sensing dataset augmentation, which is an effective means of improving the accuracy of learning models. Experimental results with high Inception Scores (IS) validate the effectiveness of the Graphical-GAN for remote sensing image synthesis.