Superpixel Based Spatial and Temporal Adaptive Reflectance Fusion Model

Abstract

At present, remote sensing images are mutually restricted in temporal and spatial resolution. A single satellite sensor cannot obtain remote sensing images with both high spatial resolution and high temporal resolution. Spatiotemporal fusion of remote sensing images is a promising method to solve this issue. The spatial and temporal adaptive reflectance fusion model (STARFM) is a widely-accepted method for spatiotemporal fusion. However, STARFM selects similar pixels in a regular rectangular window. This neighborhood window has many different land cover types, which leads to wrong selection of similar pixels. Therefore, we develop a novel spatial and temporal adaptive reflectance fusion model based on superpixel, denote by S-STARFM. In the proposed method, the target pixels to be predicted are divided into two categories, including changed pixels and unchanged pixels. Then the superpixels are used to improve the selection of similar pixels. To verify the effectiveness of S-STARFM, the moderate resolution imaging spectrometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) data are used to generate high spatiotemporal resolution images. The prediction image accuracy shows that the proposed method outperforms the STARFM.