Pansharpening in remote sensing image aims at acquiring a high-resolution multispectral (HRMS) image directly by fusing a low-resolution multispectral (LRMS) image with a panchromatic (PAN) image. The main concern is how to effectively combine the rich spectral information of LRMS image with the abundant spatial information of PAN image. Recently, many methods based on deep learning have been proposed for the pansharpening task. However, these methods usually has two main drawbacks: 1) requiring HRMS for supervised learning; and 2) simply ignoring the latent relation between the MS and PAN image and fusing them directly. To solve these problems, we propose a novel unsupervised network based on learnable degradation processes, dubbed as LDP-Net. A reblurring block and a graying block are designed to learn the corresponding degradation processes, respectively. In addition, a novel hybrid loss function is proposed to constrain both spatial and spectral consistency between the pansharpened image and the PAN and LRMS images at different resolutions. Experiments on Worldview2 and Worldview3 images demonstrate that our proposed LDP-Net can fuse PAN and LRMS images effectively without the help of HRMS samples, achieving promising performance in terms of both qualitative visual effects and quantitative metrics.
翻译:在遥感图像中,光栅系旨在直接通过将低分辨率多光谱(LRMS)图像与全色(PAN)图像混在一起,从而获得高分辨率多光谱(HRMS)图像。主要的关注是如何有效地将LRMS图像丰富的光谱信息与丰富的PAN图像空间信息结合起来。最近,提出了许多基于深层学习的各种方法,用于泛光图任务。然而,这些方法通常有两个主要缺点:(1) 需要HRMS进行监督的学习;和(2) 简单地忽视MS和PAN图像之间的潜在关系,并直接加以利用。为了解决这些问题,我们提议建立一个以可学习的降解过程为基础、称为LDP-Net的无监督的新颖、不受监督的、不受监督的网络网络。一个重新布灵格和灰色块旨在分别学习相应的降解过程。此外,还提议了一个新型混合损失功能,以限制相连接的图像与不同分辨率的PANS和LMS图像之间的空间和光谱一致性。在Worlview2和Worldview3图像上进行实验,以可学习的可监测的降解性图像为基础显示我们所拟议的图像的图像的可有效实现高质的图像和高质的图像的图像和高质的图像的图像。LDP-MAS的图像的图像,可以有效地实现高质性能的图像和高质谱系。