A detailed analysis of multi-sensor fusion of Moderate Resolution Imaging Spectroradiometer

The main aim of this paper is to develop a monitoring system for the analysis of Land cover classes (Agriculture, Urban and water). Hence in this perspective, the use of Moderate Resolution Imaging Spectroradiometer (MODIS) Satellite data is a good choice, as because of its temporal as well as spectral capability and as well as it is freely available. The problems with MODIS data are their poor spatial resolution. This problem can be minimized by application of the fusion techniques where high resolution data will be used to fuse with low resolution data. Hence in this paper, we have considered to fuse, high resolution i.e., like 15m resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data with moderate resolution i.e., like 250m Terra MODIS satellite data. The main aim of this paper is to analyze the effect of classification accuracy on major type of land cover types like agriculture, water and urban bodies with fusion of ASTER data to MODIS data. Curvelet transformation has been applied for fusion of these two satellite data and Minimum Distance classification technique has been applied for the resultant fused data. It is quantitatively observed that the overall classification accuracy of MODIS data after fusion is quite enhanced. This type of fusion technique may be quite helpful in near future to maximize the use of freely available satellite data and consequently to develop a monitoring system.

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