  The trade-off in remote sensing instruments that balances the spatial
resolution and temporal frequency limits our capacity to monitor spatial and
temporal dynamics effectively. The spatiotemporal data fusion technique is
considered as a cost-effective way to obtain remote sensing data with both high
spatial resolution and high temporal frequency, by blending observations from
multiple sensors with different advantages or characteristics. In this paper,
we develop the spatial and temporal non-local filter based fusion model
(STNLFFM) to enhance the prediction capacity and accuracy, especially for
complex changed landscapes. The STNLFFM method provides a new transformation
relationship between the fine-resolution reflectance images acquired from the
same sensor at different dates with the help of coarse-resolution reflectance
data, and makes full use of the high degree of spatiotemporal redundancy in the
remote sensing image sequence to produce the final prediction. The proposed
method was tested over both the Coleambally Irrigation Area study site and the
Lower Gwydir Catchment study site. The results show that the proposed method
can provide a more accurate and robust prediction, especially for heterogeneous
landscapes and temporally dynamic areas.

Computer Vision and Pattern Recognition