Fusion of MODIS, AVHRR and ASTER data using curvelet transform for land cover classification

With the availability of multisensor and multiresolution image data from operational Earth Observation satellites, the fusion of digital image data has become a valuable tool in land cover classification. Digital image fusion is a relatively new research field at the leading edge of available technology. It forms a rapidly developing area of research in land cover classification. It is needed that to fuse high resolution satellite data with low resolution satellite data, to enhance the classification and interpretation in low resolution satellite data. The AVHRR and MODIS data are freely available, but resolution is poor. Therefore in this paper, it is attempted to highlight the AVHRR and MODIS utility with fusion of ASTER data. In this paper, a fusion method based on the Curvelet transform is introduced. The curvelet transform represents edges more accurately, since edges play a fundamental role in image understanding, one good way to enhance spatial resolution is to enhance the edges. Curvelet-based image fusion method provides richer information in the spatial and spectral domains simultaneously.