FGF-GAN: A Lightweight Generative Adversarial Network for Pansharpening via Fast Guided Filter

Pansharpening is a widely used image enhancement technique for remote sensing. Its principle is to fuse the input high-resolution single-channel panchromatic (PAN) image and low-resolution multi-spectral image and to obtain a high-resolution multi-spectral (HRMS) image. The existing deep learning pansharpening method has two shortcomings. First, features of two input images need to be concatenated along the channel dimension to reconstruct the HRMS image, which makes the importance of PAN images not prominent, and also leads to high computational cost. Second, the implicit information of features is difficult to extract through the manually designed loss function. To this end, we propose a generative adversarial network via the fast guided filter (FGF) for pansharpening. In generator, traditional channel concatenation is replaced by FGF to better retain the spatial information while reducing the number of parameters. Meanwhile, the fusion objects can be highlighted by the spatial attention module. In addition, the latent information of features can be preserved effectively through adversarial training. Numerous experiments illustrate that our network generates high-quality HRMS images that can surpass existing methods, and with fewer parameters.

1. INTRODUCTION

Pansharpening (also known as remote sensing image fusion) is a hot issue in environmental monitoring and multi-modal image fusion. The purpose of this task is to fuse the spatial/spectral information from source images including high-resolution single-channel panchromatic (PAN) images and low-resolution multi-spectral (LRMS) images. In the end, high-resolution multi-spectral (HRMS) images with the same size as PAN images and the same channel as LRMS images are obtained.