Spatial and Temporal Image Fusion via Regularized Spatial Unmixing

Abstract

A novel spatial and temporal data fusion model based on regularized spatial unmixing was developed to generate Landsat-like synthetic data with the fine spatial resolution of Landsat Enhanced Thematic Mapper Plus (Landsat ETM+) data and the high temporal resolution of Moderate Resolution Imaging Spectroradiometer (MODIS) data. The proposed approach is based on the conventional spatial unmixing technique, but modified to include prior class spectra, which are estimated from pairs of MODIS and Landsat data using the spatial and temporal adaptive reflectance data fusion model. The method requires the optimization of the following three parameters: the number of classes of Landsat data, the neighborhood size of the MODIS data for spatial unmixing, and a regularization parameter added to the cost function to reduce unmixing error. Indexes of relative dimensionless global error in synthesis (ERGAS) were used to determine the best combination of the three parameters by evaluating the quality of the fused result at both Landsat and MODIS spatial resolutions. The experimental results with observed satellite data showed that the proposed approach performs better than conventional unmixing-based fusion approaches with the same parameters.