Spatiotemporal Reflectance Fusion via Sparse Representation

This paper presents a novel model for blending remote sensing data of high spatial resolution (HSR), taken at infrequent intervals, with those available frequently but at low spatial resolution (LSR) in the context of monitoring and predicting changes in land usage and phenology. Named “SParse-representation-based SpatioTemporal reflectance Fusion Model” (SPSTFM), the model has been developed for predicting HSR surface reflectances through data blending with LSR scenes. Remarkably, this model forms a unified framework for fusing remote sensing images with temporal reflectance changes, phenology change (e.g., seasonal change of vegetation), or type change (e.g., conversion of farmland to built-up area), by establishing correspondences between structures within HSR images of given areas and their corresponding LSR images. Such corresponding relationship is achieved by means of the sparse representation, specifically by jointly training two dictionaries generated from HSR and LSR difference image patches and sparse coding at the reconstruction stage. SPSTFM was tested using both a simulated data set and an actual data set of Landsat Enhanced Thematic Mapper Plus-Moderate Resolution Imaging Spectroradiometer acquisitions. It was also compared with other related algorithms on two types of data: images primarily with phenology change and images primarily with land-cover type change. Experimental results demonstrate the superiority of SPSTFM in capturing surface reflectance changes on both categories of images.