Dong Wenqian, Hou Shaoxiong, Xiao Song, Qu Jiahui, Du Qian, Li Yunsong
IEEE Trans Neural Netw Learn Syst. 2022 Dec;33(12):7303-7317. doi: 10.1109/TNNLS.2021.3084745. Epub 2022 Nov 30.
Hyperspectral (HS) pansharpening is of great importance in improving the spatial resolution of HS images for remote sensing tasks. HS image comprises abundant spectral contents, whereas panchromatic (PAN) image provides spatial information. HS pansharpening constitutes the possibility for providing the pansharpened image with both high spatial and spectral resolution. This article develops a specific pansharpening framework based on a generative dual-adversarial network (called PS-GDANet). Specifically, the pansharpening problem is formulated as a dual task that can be solved by a generative adversarial network (GAN) with two discriminators. The spatial discriminator forces the intensity component of the pansharpened image to be as consistent as possible with the PAN image, and the spectral discriminator helps to preserve spectral information of the original HS image. Instead of designing a deep network, PS-GDANet extends GANs to two discriminators and provides a high-resolution pansharpened image in a fraction of iterations. The experimental results demonstrate that PS-GDANet outperforms several widely accepted state-of-the-art pansharpening methods in terms of qualitative and quantitative assessment.
高光谱（HS）全色锐化对于提高用于遥感任务的HS图像的空间分辨率至关重要。HS图像包含丰富的光谱内容，而全色（PAN）图像提供空间信息。HS全色锐化使得生成具有高空间和光谱分辨率的全色锐化图像成为可能。本文基于生成性对偶对抗网络（称为PS - GDANet）开发了一种特定的全色锐化框架。具体而言，全色锐化问题被表述为一个对偶任务，可以通过具有两个判别器的生成对抗网络（GAN）来解决。空间判别器迫使全色锐化图像的强度分量与PAN图像尽可能一致，而光谱判别器有助于保留原始HS图像的光谱信息。PS - GDANet不是设计一个深度网络，而是将GAN扩展为两个判别器，并在几次迭代中提供高分辨率的全色锐化图像。实验结果表明，在定性和定量评估方面，PS - GDANet优于几种广泛接受的最新全色锐化方法。
