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VSDF: A variation-based spatiotemporal data fusion method
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DOI：10.1016/j.rse.2022.113309.png)
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摘要
En 中文
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.
Keyword:
Data fusion
Spatiotemporal fusion
Landsat
MODIS
Guided filter
Change detection
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IF:
11.4
论文数:
1.1W
被引数:
9.4W
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