الفهرس 
AcknowledgementsOnly 14 pages are availabe for public view
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Abstract
Geometric correction is used to correct the registration errors in remotely sensed images. These images are often compared to ground control points (GCPs) either by using an accurate map (image to map) or using another geo-referenced image (image to image) and then resampled. Accordingly, the exact locations and the appropriate pixel values can be calculated in more accurate, time-wise and effortless manner. In the traditional methods, the GCPs are manually selected and then the transformation models are applied which yield time consuming and less accurate processes. The objective of this work was to develop an automatic approach for image registration based on another geo-referenced image using five feature extraction models. This was in addition to studying the impact of image registration on the accuracy of vegetation cover based on the Normalized Difference Vegetation Index (NDVI). They are Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), Discrete Wavelet Transforms (DWT), (SIFT & DWT), and (SURF & DWT). The GCPs were selected based on the least-squares adjustments as the basis for improving the spatial accuracy of all the linking points in both images. The obtained results showed that all of the studied models had higher accuracy in image registration with Root Mean Square Error (RMSE) less than 0.5. However, the combination between (SURF & DWT) showed the lowest RMSE value and consequently the highest accuracy in calculating the areas of vegetation cover. In conclusion, the developed automated image registration method provides more accurate results and saves time, money and effort.