Time-series remote sensing data are important in monitoring land surface dynamics. Due to technical limitations, satellite sensors have a trade-off between temporal, spatial and spectral resolutions when acquiring remote sensing images. In order to obtain remote sensing images with high spatial resolution and high temporal frequency, spatiotemporal fusion methods have been developed. In this paper, we propose a Linear Spectral Unmixing-based Spatiotemporal Data Fusion Model (LSUSDFM) for spatial and temporal data fusion. In this model, the endmember abundance of the low-resolution image pixel is calculated based on that of the high-resolution image by the spectral mixture analysis. The endmember spectrum signals of low-resolution images are then calculated continuously within an optimized moving window. Subsequently, the fused image is reconstructed according to the endmember spectrum and its corresponding abundance map. A simulated dataset and real satellite images are used to test the fusion model, and the fusion results are compared with a current spectral unmixing based downscaling fusion model (SUDFM). Our experimental work shows that, compared to the SUDFM, the proposed LSUSDFM can achieve better quality and accuracy of fused images, especially in effectively eliminating the “plaque” phenomenon in the results by the SUDFM. The LSUSDFM has great potential in generating images with both high spatial resolution and high temporal frequency, as well as increasing the number of spectral bands of the high spatial resolution data.

时间序列遥感数据对于监测地表动态非常重要。由于技术限制，卫星传感器在获取遥感图像时需要在时间，空间和光谱分辨率之间进行权衡。为了获得具有高空间分辨率和高时间频率的遥感图像，已经开发了时空融合方法。在本文中，我们提出了一种基于线性光谱分解的时空数据融合模型（LSUSDFM），用于时空数据融合。在该模型中，通过光谱混合分析，基于高分辨率图像的末端成员丰度，计算出低分辨率图像像素的末端成员丰度。然后在优化的移动窗口内连续计算低分辨率图像的端成员光谱信号。后来，根据端成员谱及其对应的丰度图重建融合图像。仿真的数据集和真实的卫星图像用于测试融合模型，并将融合结果与当前基于频谱分解的降尺度融合模型（SUDFM）进行比较。我们的实验工作表明，与SUDFM相比，所提出的LSUSDFM可以实现更好的融合图像质量和准确性，尤其是在有效消除SUDFM结果中的“斑块”现象方面。LSUSDFM在生成具有高空间分辨率和高时间频率的图像以及增加高空间分辨率数据的光谱带数量方面具有巨大潜力。仿真的数据集和真实的卫星图像用于测试融合模型，并将融合结果与当前基于频谱分解的降尺度融合模型（SUDFM）进行比较。我们的实验工作表明，与SUDFM相比，所提出的LSUSDFM可以实现更好的融合图像质量和准确性，尤其是可以有效消除SUDFM结果中的“斑块”现象。LSUSDFM在生成具有高空间分辨率和高时间频率的图像以及增加高空间分辨率数据的光谱带数量方面具有巨大潜力。仿真的数据集和真实的卫星图像用于测试融合模型，并将融合结果与当前基于频谱分解的降尺度融合模型（SUDFM）进行比较。我们的实验工作表明，与SUDFM相比，所提出的LSUSDFM可以实现更好的融合图像质量和准确性，尤其是在有效消除SUDFM结果中的“斑块”现象方面。LSUSDFM在生成具有高空间分辨率和高时间频率的图像以及增加高空间分辨率数据的光谱带数量方面具有巨大潜力。提出的LSUSDFM可以实现更好的融合图像质量和准确性，尤其是在有效消除SUDFM结果中的“斑块”现象方面。LSUSDFM在生成具有高空间分辨率和高时间频率的图像以及增加高空间分辨率数据的光谱带数量方面具有巨大潜力。提出的LSUSDFM可以达到更好的融合图像质量和准确性，尤其是在有效消除SUDFM结果中的“斑块”现象方面。LSUSDFM在生成具有高空间分辨率和高时间频率的图像以及增加高空间分辨率数据的光谱带数量方面具有巨大潜力。