A Noise Proof Strategy for Spatio-Temporal Fusion of Remote Sensing Imagery

Abstract

Spatio-temporal fusion is a feasible way to generating the synthetic remote sensing data with high spatial resolution and high temporal resolution simultaneously by blending the fine and coarse resolution satellite images. To date, dozens of spatio-temporal fusion approaches have been developed. A basic rule of these approaches is the bands of coarse and fine images must be corresponding, which means the quality of fused images depends on that of both fine and coarse images. In the literature, the MODIS images are the most wildly used coarse images in spatio-temporal fusion. However, the MODIS images may suffer from serious stripe noises in the short-wave infrared-1 and short-wave infrared-2 bands, which will lead to undesired results of spatio-temporal fusion. To address this problem, we develop a noise proof strategy in this paper, which takes advantage of the spectral correlation of base fine image to remove the stripe noises of the base MODIS image, then the spatial correlation of base MODIS image is exploited to restore the MODIS image of the predicted time. Finally, the reconstructed MODIS images are fused with the base fine image to predict the missing fine images. The strategy is tested via real Landsat and MODIS images, and the experimental result demonstrates it is not only effective in removing the stripe noises of MDOIS short-wave infrared-1 and short-wave infrared-2 bands, but also able to improve the fusion accuracy.