Abstract

ABSTRACT: Remote sensing images fusion can not only improve the spatial resolution for the original multispectral image, but should also preserve the spectral information to a certain degree. Color transformation based image fusion methods have been implemented in various papers and this methods shows good spectral retention. This paper aims to implement pixel-level image fusion based on mathematical and wavelet transform image fusion methods and find out their capacity to improve spatial and spectral information. For this purpose different methods such as Averaging method, Multiplicative method, Brovey method, DWT method are implemented. Performance of this methods is evaluated with the help of assessment parameters such entropy, standard deviation, RMSE And PSNR. Experimental results shows that images merge by multiplicative and wavelet based method showed higher spatial resolution and better spectral features than the original image Keyword: Image fusion, Brovey method, DWT, Entropy, RMSE, PSNR. I.INTRODICTION Remote sensing offers a wide variety of image data with different characteristics in terms of temporal, spatial, radiometric and Spectral resolutions. For optical sensor systems, imaging systems somehow offer a trade off between high spatial and high spectral resolution, and no single system offers both. Hence, in the remote sensing community, an image with "greater quality" often means higher spatial or higher spectral resolution, which can only be obtained by more advanced sensors . The designing of a sensor to provide both high spatial and spectral resolutions is limited by the trade off between spectral resolution, spatial resolution, and signal-to-noise ratio of sensor. It is, therefore, necessary and very useful to be able to merge images with higher spectral information and higher spatial information [1]. Image fusion techniques can be classified into three categories depending on the stage at which fusion takes place; it is often divided into three levels, namely: pixel level, feature level and decision level of representation II.MULTIPLICATIVE METHOD The Multiplication model combines two data sets by multiplying each pixel in each band of the MS data by the corresponding pixel of the Pan data.