A Spatial and Temporal Non-Local Filter Based Data Fusion
This addresses the problem of limited monitoring capacity in remote sensing for researchers and practitioners, but it is incremental as it builds on existing spatiotemporal data fusion techniques.
The paper tackled the trade-off between spatial resolution and temporal frequency in remote sensing by developing the Spatial and Temporal Non-Local Filter Based Fusion Model (STNLFFM) to blend data from multiple sensors, resulting in more accurate and robust predictions, especially for heterogeneous and dynamic landscapes, as tested on two study sites.
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.