Hyperspectral single image super-resolution (HS-SISR) aims to enhance the spatial resolution of hyperspectral images to fully exploit their spectral information. While considerable progress has been made in this field, most existing methods are supervised and require ground truth data for training-data that is often unavailable in practice. To overcome this limitation, we propose a novel unsupervised training framework for HS-SISR, based on synthetic abundance data. The approach begins by unmixing the hyperspectral image into endmembers and abundances. A neural network is then trained to perform abundance super-resolution using synthetic abundances only. These synthetic abundance maps are generated from a dead leaves model whose characteristics are inherited from the low-resolution image to be super-resolved. This trained network is subsequently used to enhance the spatial resolution of the original image's abundances, and the final super-resolution hyperspectral image is reconstructed by combining them with the endmembers. Experimental results demonstrate both the training value of the synthetic data and the effectiveness of the proposed method.

翻译：高光谱单图像超分辨率（HS-SISR）旨在提升高光谱图像的空间分辨率，以充分利用其光谱信息。尽管该领域已取得显著进展，但现有方法大多为有监督方法，需要真实数据作为训练数据，而这些数据在实际中往往难以获取。为克服这一限制，我们提出了一种基于合成丰度数据的新型无监督训练框架用于HS-SISR。该方法首先将高光谱图像解混为端元与丰度。随后，仅使用合成丰度训练一个神经网络来执行丰度超分辨率。这些合成丰度图通过死叶模型生成，该模型的特性继承自待超分辨的低分辨率图像。训练后的网络随后用于提升原始图像丰度的空间分辨率，最终的超分辨率高光谱图像通过将处理后的丰度与端元结合重建得到。实验结果验证了合成数据的训练价值以及所提方法的有效性。