VSDF: A variation-based spatiotemporal data fusion method
VSDF: A variation-based spatiotemporal data fusion method
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DOI：
10.1016/j.rse.2022.113309
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发表时间：
2022-12
影响因子：
13.5
通讯作者：
Chen Xu;Xiaoping Du;Zhenzhen Yan;Jun-jie Zhu;Shuning Xu;Xiangtao Fan
中科院分区：
文献类型：
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作者：
Chen Xu;Xiaoping Du;Zhenzhen Yan;Jun-jie Zhu;Shuning Xu;Xiangtao Fan
时空数据的融合为提高遥感数据的时空分辨率提供了可能。然而，当前时空数据融合方法的性能受到几个方面的影响，例如，(1)土地覆盖突然变化的恢复，（2）恢复详细的空间信息，以及（3）需要减少与传感器之间的性能差异有关的副作用。针对上述问题，本研究提出一种基于变分的时空数据融合方法。在VSDF中，一个丰富的变化分类（AVC）被用来明确地识别光谱分解的土地覆盖变化。此外，还引入特征级融合来增强空间结构，即，边缘和纹理。此外，提出了一个相对可靠性指标（RRI）来指导预测过程，以降低输入数据集的不确定性。参考全面性能评估（阿帕）指标，将VSDF的性能与五种流行方法进行比较，即，时空自适应反射率融合模型（STARFM）、灵活时空数据融合（FSDAF）、FSDAF 2.0、可靠和自适应时空数据融合（RASDF）和Fit-FC（回归模型拟合、空间滤波和残差补偿）。实验结果表明，与基准方法相比，VSDF能更准确地预测土地覆盖的时间变化，并具有更好的空间细节。因此，VSDF有潜力为全球遥感研究提供精确的高时空分辨率模拟。
The fusion of spatiotemporal data provides the possibility to improve both the spatial and temporal resolution of remote sensing data. Nevertheless, the performance of current spatiotemporal data fusion methods is affected by several aspects, e.g., (1) retrieval of abrupt land cover changes, (2) recovery of detailed spatial information, and (3) the need to reduce side effects related to the performance differences between sensors. Concerning the above aspects, this study proposes the use of a Variation-based Spatiotemporal Data Fusion (VSDF) method. In VSDF, an abundant variation classification (AVC) is used to identify explicitly the land cover changes for spectral unmixing. In addition, feature-level fusion is introduced to strengthen the spatial structures, i.e., the edges and texture. Furthermore, a relative reliability index (RRI) is proposed to guide the prediction process to lower the uncertainty of the input datasets. With reference to the all-round performance assessment (APA) metrics, the performance of VSDF was compared with five popular methods, i.e., Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Flexible Spatiotemporal DAta Fusion (FSDAF), FSDAF 2.0, Reliable and Adaptive Spatiotemporal Data Fusion (RASDF), and Fit-FC (regression model Fitting, spatial Filtering and residual Compensation). The experimental results demonstrated that VSDF can realize a more accurate prediction of temporal land cover changes with better spatial detail than the benchmark methods. Consequently, VSDF has the potential to generate accurate high-spatiotemporal-resolution simulations for global remote sensing studies.