الفهرس 
Basic Concept of Remote SensingOnly 14 pages are availabe for public view
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Abstract
In recent years, many satellites were launched, which provide various types of
images about the earth surface. These images have different spectral and spatial
characteristics. Super-Resolution (SR) is a fusion process for reconstructing a HR image
from several lower resolution (LR) images covering the same region in the world. If the
images are sub-pixel shifted, then each LR image contains different information. SR
extends the classical single-frame image reconstruction/restoration methods by
simultaneously utilizing information from multiple observed images to achieve
resolutions higher than those of the original images. Image fusion techniques are used
extensively to combine different images having complementary information into one
single composite. The fused image has rich information that will improve the
performance of image analysis algorithms. Pan-sharpening is a pixel level fusion
technique used to increase the spatial resolution of the multispectral (MS) image using
spatial information from the high resolution (HR) panchromatic (Pan) image while
preserving the spectral information in the MS image. This research proposes a new image super-resolution restoration algorithm. The
development of the algorithm is based on the improvement of the classical projection
onto convex set (POCS) algorithm and the stationary wavelet transform (SWT) to restore
a super-resolution image from Egyptsat-1 low resolution (LR) images. Egyptsat-1 bands
have inconsistent sub-pixel shift. This inconsistent shift between the bands can be
changed into reliable shift by adaptive interpolation. Then, decomposition of high
frequency sub-bands is generated using (SWT). The POCS iteration is used to restore
high-resolution (HR) sub-bands from every LR images of the wavelet decomposition, and
a HR image is reconstructed by inverse wavelet transform. Consequences show that the
proposed methods yields significant spatial resolution improvements. The reconstructed
image is evaluated by the Peak Signal to Noise Ratio (PSNR), Root Main Square Error (RMSE), Entropy, and Objective Fusion Measures. Different quantitative measures for
the proposed method were assessed and tested with some implemented commonly used
SR methods. The experimental results showed that this method can improve the ability of
fusing different image information, and the visual and quantitative evaluations verify the
usefulness and effectiveness of the proposed methodology.