  Remote sensing image fusion technology (pan-sharpening) is an important means
to improve the information capacity of remote sensing images. Inspired by the
efficient arameter space posteriori sampling of Bayesian neural networks, in
this paper we propose a Bayesian Generative Adversarial Network based on
Preconditioned Stochastic Gradient Langevin Dynamics (PGSLD-BGAN) to improve
pan-sharpening tasks. Unlike many traditional generative models that consider
only one optimal solution (might be locally optimal), the proposed PGSLD-BGAN
performs Bayesian inference on the network parameters, and explore the
generator posteriori distribution, which assists selecting the appropriate
generator parameters. First, we build a two-stream generator network with PAN
and MS images as input, which consists of three parts: feature extraction,
feature fusion and image reconstruction. Then, we leverage Markov discriminator
to enhance the ability of generator to reconstruct the fusion image, so that
the result image can retain more details. Finally, introducing Preconditioned
Stochastic Gradient Langevin Dynamics policy, we perform Bayesian inference on
the generator network. Experiments on QuickBird and WorldView datasets show
that the model proposed in this paper can effectively fuse PAN and MS images,
and be competitive with even superior to state of the arts in terms of
subjective and objective metrics.
