Considerable work has been dedicated to hyperspectral single image super-resolution to improve the spatial resolution of hyperspectral images and fully exploit their potential. However, most of these methods are supervised and require some data with ground truth for training, which is often non-available. To overcome this problem, we propose a new unsupervised training strategy for the super-resolution of hyperspectral remote sensing images, based on the use of synthetic abundance data. Its first step decomposes the hyperspectral image into abundances and endmembers by unmixing. Then, an abundance super-resolution neural network is trained using synthetic abundances, which are generated using the dead leaves model in such a way as to faithfully mimic real abundance statistics. Next, the spatial resolution of the considered hyperspectral image abundances is increased using this trained network, and the high resolution hyperspectral image is finally obtained by recombination with the endmembers. Experimental results show the training potential of the synthetic images, and demonstrate the method effectiveness.

翻译：高光谱单图像超分辨率研究已投入大量工作，旨在提升高光谱图像的空间分辨率并充分挖掘其潜力。然而，现有方法大多为监督式训练，需要具备真实标签的数据进行训练，而此类数据往往难以获取。为克服此问题，本文提出一种基于合成丰度数据的高光谱遥感图像无监督超分辨率训练策略。该方法首先通过解混将高光谱图像分解为丰度与端元；随后利用合成丰度训练丰度超分辨率神经网络——这些合成丰度通过枯叶模型生成，能够精确模拟真实丰度的统计特性；接着，使用训练好的网络提升目标高光谱图像丰度的空间分辨率，最终通过与端元重组得到高分辨率高光谱图像。实验结果表明合成图像具有优异的训练潜力，并验证了本方法的有效性。