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In this study, there is examined filtering based pansharpening methods which means of using several 2D FIR filters in Fourier domain which implies that the filters are applied after taking 2D Discrete Fourier Transform of both multispectral and panchromatic image and after the pansharpening process in Fourier domain, the resulting pansharpened image is obtained with an inverse 2D DFT. In addition, these methods are compared with commonly used fusion methods which are combined as modulation based and component substitution based methods. The algorithms are applied to SPOT 6 co-registered image couples that were acquired simultaneously. Couples are chosen for three different regions which are a city image (Gebze/Turkey), a forest image (Istanbul/Turkey) and an agriculture field image (Sanliurfa/Turkey) in order to analyse the methods in different regional characteristics. These methods are compared by the fusion quality assessments that have common acceptance in community. The results of these quality assessments shows the filtering based methods had the best scores among the traditional methods.

ABSTRACT:High resolution level in satellite imagery came with its fundamental problem as big amount of telemetry data which is to be stored after the downlink operation. Moreover, later the post-processing and image enhancement steps after the image is acquired, the file sizes increase even more and then it gets a lot harder to store and consume much more time to transmit the data from one source to another; hence, it should be taken into account that to save even more space with file compression of the raw and various levels of processed data is a necessity for archiving stations to save more space. Lossless data compression algorithms that will be examined in this study aim to provide compression without any loss of data holding spectral information.

ABSTRACT:High resolution level in satellite imagery came with its fundamental problem as big amount of telemetry data which is to be stored after the downlink operation. Moreover, later the post-processing and image enhancement steps after the image is acquired, the file sizes increase even more and then it gets a lot harder to store and consume much more time to transmit the data from one source to another; hence, it should be taken into account that to save even more space with file compression of the raw and various levels of processed data is a necessity for archiving stations to save more space. Lossless data compression algorithms that will be examined in this study aim to provide compression without any loss of data holding spectral information.