论文标题
基于贝叶斯甘基的遥感图像融合
Remote sensing image fusion based on Bayesian GAN
论文作者
论文摘要
遥感图像融合技术（Pan-Sharpening）是提高遥感图像信息能力的重要手段。受贝叶斯神经网络的有效芳香空间后验抽样的启发，在本文中，我们提出了一个基于预处理的随机梯度Langevin Dynamics（PGSLD-BGAN）的贝叶斯生成对抗网络，以改善pan-sharpharpenting任务。与许多仅考虑一个最佳解决方案（可能是本地最佳）的传统生成模型不同，拟议的PGSLD-BGAN在网络参数上执行贝叶斯推断，并探索发电机后验分布，该分布有助于选择适当的生成器参数。首先，我们构建了一个带有PAN和MS图像作为输入的两流生成器网络，其中包括三个部分：特征提取，功能融合和图像重建。然后，我们利用马尔可夫歧视器来增强发电机重建融合图像的能力，从而使结果图像可以保留更多详细信息。最后，我们引入了预处理的随机梯度Langevin Dynamics策略，我们对发电机网络执行贝叶斯推断。 QuickBird和Worldview数据集的实验表明，本文提出的模型可以有效地融合PAN和MS图像，并且在主观和客观指标方面，与优于艺术的竞争能力相比。
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.
