Remote sensing image fusion based on two-stream fusion network

Abstract

Remote sensing image fusion (also known as pan-sharpening) aims at generating a high resolution multi-spectral (MS) image from inputs of a high spatial resolution single band panchromatic (PAN) image and a low spatial resolution multi-spectral image. Inspired by the astounding achievements of convolutional neural networks (CNNs) in a variety of computer vision tasks, in this paper we propose a Two-stream Fusion Network (TFNet) to address the problem of pan-sharpening. Unlike many previous CNN based methods that consider pan-sharpening as a super-resolution problem and perform pan-sharpening through mapping the stacked PAN and MS to the target high resolution MS image, the proposed TFNet aims to fuse PAN and MS images in feature domain and reconstruct the pan-sharpened image from the fused features. The TFNet mainly consists of three parts. The first part is comprised of two networks extracting features from PAN and MS images, respectively. The subsequent network fuses them together to form compact features that represent both spatial and spectral information of PAN and MS images, simultaneously. Finally, the desired high spatial resolution MS image is recovered from the fused features through an image reconstruction network. Experiments on Quickbird and GaoFen-1 images demonstrate that the proposed TFNet can fuse PAN and MS images effectively, and produce pan-sharpened images competitive with even superior to state of the arts.