无需真实标签，用合成数据训练超分辨模型提升遥感图像分辨率
Unsupervised Super-Resolution of Hyperspectral Remote Sensing Images Using Fully Synthetic Training
- 通过解混获取端元和丰度，用死叶模型生成逼真合成丰度数据
- 在合成数据上训练网络，使高光谱图像空间分辨率显著提升
- 适合缺乏真实标注数据的遥感图像超分辨任务
为提升高光谱遥感图像的空间分辨率并充分挖掘其潜力，已有大量研究致力于单图超分辨。然而，多数方法依赖带真实标签的数据进行监督训练，而此类数据往往难以获取。为此，本文提出一种基于合成丰度数据的无监督超分辨训练策略。首先通过解混将高光谱图像分解为丰度与端元；接着利用死叶模型生成符合真实丰度统计特性的合成丰度数据，并以此训练丰度超分辨神经网络；随后，使用训练好的网络提升原始图像的丰度空间分辨率，并结合端元重构出高分辨率高光谱图像。实验结果验证了合成数据的训练潜力，证明了该方法的有效性。
原文摘要 · Abstract (English)
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.
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