A robust adaptive spatial and temporal image fusion model for complex land surface changes

Abstract

Spatial and temporal satellite image fusion (STIF) has provided a feasible alternative for generating imagery with both high spatial and temporal resolution, thus expanding the applications of existing satellite sensors. However, a critical challenge confronting the further development of STIF is to systematically and robustly address complex land surface changes, which include land cover changes without shape changes (e.g., crop rotation) and land cover changes with shape changes (e.g., urban expansion), in addition to conventional land surface changes (e.g., phenological changes of vegetation). This paper presents the Robust Adaptive Spatial and Temporal Fusion Model (RASTFM) to tackle this challenge with one prior pair of MODIS-Landsat images. In RASTFM, land surface changes are reorganized into non-shape changes (including phenological changes and land cover changes without shape changes) and shape changes (i.e., land cover changes with shape changes), which are handled differently. However, both non-shape changes and shape changes are predicted through a Non-Local Linear Regression (NL-LR) of the subject pixel's similar neighbors. A regression based high-pass modulation is also performed as a post-processing step to improve both the spatial details and spectral fidelity of the predicted Landsat image. Unlike other STIF models (e.g., the Spatial and Temporal Adaptive Reflectance Fusion Model, STARFM), RASTFM can find similar neighboring pixels more precisely through a non-local searching strategy and derives the weights of similar neighbors more rigorously via a linear regression model. As both non-shape and shape changes are treated based on the regression of similar neighboring pixels, the land surface changes are processed in a unified manner. Experiments that use one simulated and three actual MODIS-Landsat datasets featured by different types of land surface changes were conducted to demonstrate the performance of RASTFM. Comparisons with the state-of-the-art STIF models, including weighted function, unmixing and dictionary-learning methods, show that NL-LR based RASTFM can capture the land surface changes in various landscapes more accurately and robustly in a unified manner, thereby facilitating the continuous and detailed monitoring of complex and diverse land surface dynamics.