A Spatial and Temporal Non-Local Filter Based Data Fusion

TL;DR

This paper introduces a novel spatiotemporal data fusion method using non-local filters to improve the accuracy of remote sensing data in complex and dynamic landscapes, effectively combining high spatial and temporal resolution data.

Contribution

The paper develops the STNLFFM model that leverages spatiotemporal redundancy and non-local filtering to enhance remote sensing data fusion accuracy, especially in heterogeneous and dynamic environments.

Findings

Improved prediction accuracy in complex landscapes.

Robust performance in temporally dynamic areas.

Effective fusion of multi-sensor data.

Abstract

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.