Unsharp masking based pansharpening of high resolution satellite imagery

In Pansharpening process, the basic procedure is to merge a high spatial resolution panchromatic image with a multi-spectral image with low spatial resolution. The spatial resolution of the pansharpened image is obtained from the panchromatic data while the color information and spectral resolution of the pansharpened image is acquired from the multi-spectral data. Within the scope of the thesis, twelve different pansharpening algorithms were tested on four different RASAT data sets. The spectral and spatial attributes of pansharpened image were measured via using eight different numerical performance criterions. Furthermore, a new preprocessing method, called Unsharp Masking is introduced. The Unsharp masked Pan image and the multispectral image are combined with the each pansharpening method to obtain images that are sharpened and increased in resolution. It has been observed that the Unsharp Masking technique produces approximately the same performance result compared to other methods but considerably increases the sharpness of the image. The obtained numerical performance results demonstrate the optimized High Pass Filter method give the sharpest image, while the Hyperspace Color Space method maintains the sharpness to a certain degree, besides it has been determined that carries the best color information.