Super resolution (SR) refers to generation of a High Resolution (HR) image from a decimated, blurred, low-resolution (LR) image set, which can be either a single frame or multi-frame that contains a collection of several images acquired from slightly different views of the same observation area. In this study, we propose a novel application of tri-stereo Remote Sensing (RS) satellite images to the super resolution problem. Since the tri-stereo RS images of the same observation area are acquired from three different viewing angles along the flight path of the satellite, these RS images are properly suited to a SR application. We first estimate registration between the chosen reference LR image and other LR images to calculate the sub pixel shifts among the LR images. Then, the warping, blurring and down sampling matrix operators are created as sparse matrices to avoid high memory and computational requirements, which would otherwise make the RS-SR solution impractical. Finally, the overall system matrix, which is constructed based on the obtained operator matrices is used to obtain the estimate HR image in one step in each iteration of the SR algorithm. Both the Laplacian and total variation regularizers are incorporated separately into our algorithm and the results are presented to demonstrate an improved quantitative performance against the standard interpolation method as well as improved qualitative results due expert evaluations.
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.