  Spatiotemporal fusion aims to improve both the spatial and temporal
resolution of remote sensing images, thus facilitating time-series analysis at
a fine spatial scale. However, there are several important issues that limit
the application of current spatiotemporal fusion methods. First, most
spatiotemporal fusion methods are based on pixel-level computation, which
neglects the valuable object-level information of the land surface. Moreover,
many existing methods cannot accurately retrieve strong temporal changes
between the available high-resolution image at base date and the predicted one.
This study proposes an Object-Based Spatial Unmixing Model (OBSUM), which
incorporates object-based image analysis and spatial unmixing, to overcome the
two abovementioned problems. OBSUM consists of one preprocessing step and three
fusion steps, i.e., object-level unmixing, object-level residual compensation,
and pixel-level residual compensation. OBSUM can be applied using only one fine
image at the base date and one coarse image at the prediction date, without the
need of a coarse image at the base date. The performance of OBSUM was compared
with five representative spatiotemporal fusion methods. The experimental
results demonstrated that OBSUM outperformed other methods in terms of both
accuracy indices and visual effects over time-series. Furthermore, OBSUM also
achieved satisfactory results in two typical remote sensing applications.
Therefore, it has great potential to generate accurate and high-resolution
time-series observations for supporting various remote sensing applications.
