Abstract: Due to sensor material and satellite orbit parameters, spatial- and temporal- resolution, which it is hard to balance, are inherent characteristics of remotely sensed satellite-sensors and the issue of multitemporal data reconstruction from high resolution satellites has still been a key limitation for its widely application. Considering its effective composite of image information from spatial, spectral and temporal dimension, spatiotemporal fusion technology of multi-source remotely sensed images gets a fast-developing in the last decade and has been becoming a powerful means for solving the reconstruction of multitemporal data. Although learning-based fusion strategy generally has a higher prediction accuracy and potentially utilization than other spatiotemporal fusion methods, it probably generates high uncertainty owing to its high dependence on the process of dictionary training. Aiming at promoting prediction accuracy, computational efficiency and robustness of this fusion strategy, a spatiotemporal fusion frame designed for single high-resolution remotely sensed image is developed by a composite of atmospheric correction based on radiometric regression, data normalization conversion based on error constrains, an adaptive fusion strategy using multi-layer progression and an effective function library for sparse solution. Then remote sensing images from the domestic Gaofen-2 and Landsat-8 satellites are employed for validating the proposed fusion frame. The experimental results indicate that the proposed fusion frame not only raises the computational efficiency but also exhibits a better performance than the existing fusion models for single image pair in imagery fidelity, description of textural feature and spectral consistency.

摘要： 受制于传感器本身材料及卫星轨道参数，空间分辨率和时间分辨率是卫星遥感传感器固有的性能指标且难以兼备，使得高空间分辨率卫星的多时相数据合成问题至今仍是制约其广泛应用的关键问题之一。由于可有效综合空间-光谱-时间维的影像信息，多源遥感影像时空融合技术在近十年间得到迅速发展并已成为解决多时相数据合成问题的有力手段，其中基于学习的时空融合策略在合成精度上具有显著优势且应用潜力较高，但因其对字典训练过程的依赖程度较高而在融合过程中存在一定的不确定性。为提高基于学习的时空融合策略的预测精度、运算效率及鲁棒性，通过综合基于辐射归化的大气校正方法、基于误差约束的数据标准化转换机制、自适应多层递进融合策略以及高效的稀疏求解函数库，设计了一种适用于单时相高分辨率遥感影像的时空融合框架，并以国产高分二号卫星与Landsat-8卫星遥感影像为实验数据对该方法进行充分的对比性分析。实验结果表明，该融合框架不仅提升了运算效率，还在影像保真度、纹理特征描述以及光谱一致性等方面比当前的单数据对融合方法具有更好的重构质量。

关键词: 时空融合, 稀疏学习, 单时相, 高分二号, Landsat-8