Abstract
Hyperspectral single image super-resolution (SISR) aims to enhance spatial resolution while preserving the rich spectral information of hyperspectral images. Most existing methods rely on supervised learning with high-resolution ground truth data, which is often unavailable in practice. To overcome this limitation, we propose an unsupervised learning approach based on synthetic abundance data. The hyperspectral image is first decomposed into endmembers and abundance maps through hyperspectral unmixing. A neural network is then trained to super-resolve these maps using data generated with the dead leaves model, which replicates the statistical properties of real abundances. The final super-resolution hyperspectral image is reconstructed by recombining the super-resolved abundance maps with the endmembers. Experimental results demonstrate the effectiveness of our method and the relevance of synthetic data for training.
Abstract (translated)
高光谱单幅图像超分辨率（SISR）旨在增强空间分辨率，同时保持高光谱图像的丰富光谱信息。大多数现有的方法依赖于具有高质量地面真实数据的监督学习，而这些数据在实际应用中往往不可用。为了解决这一限制，我们提出了一种基于合成丰度数据的无监督学习方法。首先通过高光谱解混将高光谱图像分解成端元和丰度图。然后训练神经网络使用死叶片模型生成的数据来对这些地图进行超分辨率处理，该模型复制了真实丰度的统计特性。最后，通过重新组合超分辨率丰度图与端元，重建出最终的超分辨高光谱图像。实验结果证明了我们方法的有效性以及合成数据在训练中的相关性和实用性。
URL
https://arxiv.org/abs/2602.02552
