Spatial and temporal image fusion for time series modis data and multi-sensors medium resoultion data

Image fusion plays an important role for integrated usage of remotely sensed data from multi-sensors. Spectral response functions of different satellite sensors are used to normalize and to eliminate the difference between sensors. Then the ESTARFM model are applied to yield the time series high frequent images with fine spatial resolution. In the experiments, one cloud-free IRS P6 LISS image and two Landsat TM images are used as fine-resolution data, and 11 scenes of cloud-free MODIS L1B images with a spatial resolution of 250m are used as time series data. Fused results are evaluated both qualitatively and quantitatively. The results show that, the fusion algorithm based on spectral response function and ESTRAFM can be well used for merging MODIS data and fine-resolution data from multisensors. The predicted image integrated the spatial advantages of the high spatial resolution images and temporal advantages of the high temporal resolution images.