Investigating the Influence of Registration Errors on the Patch-Based Spatio-Temporal Fusion Method

• Main Authors: Liguo Wang, Xiaoyi Wang, Qunming Wang, Peter M. Atkinson
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Subjects: Downscaling; Landsat; moderate resolution imaging spectroradiometer (MODIS); registration error; sparse representation based spatio-temporal reflectance fusion model (SPSTFM); spatio-temporal fusion
• Online Access: https://ieeexplore.ieee.org/document/9220778/

Spatio-temporal fusion is a common approach in remote sensing, used to create time-series image data with both fine spatial and temporal resolutions. However, geometric registration error, which is a common problem in remote sensing relative to the ground reference, is a particular problem for multiresolution remote sensing data, especially for images with very different spatial resolutions (e.g., Landsat and MODIS images). Registration error can, thus, have a significant impact on the accuracy of spatio-temporal fusion. To the best of our knowledge, however, almost no effective solutions have been provided to-date to cope with this important issue. This article demonstrates the robustness to registration error of the existing SParse representation-based spatio-temporal reflectance fusion model (SPSTFM). Different to conventional methods that are performed on a per-pixel basis, SPSTFM utilizes image patches as the basic unit. We demonstrate theoretically that the effect of registration error on patch-based methods is smaller than for pixel-based methods. Experimental results show that SPSTFM is highly robust to registration error and is far more accurate under various registration errors relative to pixel-based methods. The advantage is shown to be greater for heterogeneous regions than for homogeneous regions, and is large for the fusion of normalized difference vegetation index data. SPSTFM, thus, offers the remote sensing community a crucial tool to overcome one of the longest standing challenges to the effective fusion of remote sensing image time-series.