Fusion of ASTER image data for enhanced mapping of landcover features K. Vani Institute of Remote Sensing Anna University, Chennai vani@annauniv.edu S. Sanjeevi Center for Geoscience and Engineering, Anna University, Chennai ssanjeevi@annauniv.edu Introduction Landuse and Landcover mapping using remotely sensed image data involves delineation of the constituent units. Such a delineation, however, is dependent on the wavelength region in which they are imaged and also the spatial resolution of the imaging sensor. It is well known that the visible and near infrared bands provide information on the common land cover features and such as water, soil and vegetation. But the spectral signatures of soil and rock units suggest that the SWIR region would give better information about them Goward (1994). The limitation in using the SWIR band is that only very low spatial resolution images could be obtained (viz. 70m spatial resolution of IRS 1C band 4 ). One way of obtaining better information about soil and rock units, apart from the water and vegetation features, is to use the information obtained from both SWIR and VNIR bands. Multisensor image fusion is one of the techniques that has proved its potential in bringing out maximum information about most of the landcover features. Image fusion may be defined as a formal frame work in which are expressed means and tools for the alliance of data originating from different sources. It aims at obtaining information of greater quality; the exact definition of greater quality will depend upon application.” Wald (1999). The fused image should have more complete information that is more useful for human or machine perception. The common objectives of fusion are (i) to extract all the useful information from the source images (ii) to avoid artifacts or inconsistencies, which will distract human observers or the following processing and (iii) to be reliable and robust to imperfection such as misregistration. Image fusion improves the interpretability of satellite images. Image data obtained from different types of sensors provide complementary information about a scene. Image data fusion helps us to extract maximum information from the data set in such a way as to achieve optimal resolution in the spatial and spectral domain. The detection and the recognition of objects in the scene will be done with minimum error probability where the redundant information will improve reliability, and the complementary information will improve capability.  Fig 1: VINR
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