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Cairo, Egypt

Zaghloul E.A.,National Authority for Remote Sensing and Space science NARSS | Hassan S.M.,Data Reception | Bahy El-Dein A.M.,National Authority for Remote Sensing and Space science NARSS | Elbeih S.F.,National Authority for Remote Sensing and Space science NARSS
Egyptian Journal of Remote Sensing and Space Science | Year: 2013

Dakhla Oasis is the second provincial oasis in Al-Wadi El-Gedid Governorate in the Western Desert of Egypt. Dakhla contains several springs and wells, the most important of which are Mut Wells. Deir El - Hagar playa, located about 45 km to the northwest of Mut, the capital of the oasis, is the most important playa where the Roman Deir El-Hagar Temple is located. The Mid-Pleistocene to Holocene exposed lacustrine sediments provide evidence for a more humid climate than that present today. The main aim of this work is to use the High Pass Filter (HPF) image processing technique to enhance and extract archaeological remains and ancient irrigation canals from an Egyptsat-1 2010 satellite image in addition to link between the locations of these canals and the source of the water that replenished these canals. The enhanced images as well as the 3D prospective view indicate that the playa is a semicircular basin covered by extensive lake deposits that owed its water to the natural flowing springs, located 2 km to the south, and rainfall over the depression during the Terminal Paleolithic and Neolithic times. The significance of this research is to highlight how our ancestors were brilliant so as to utilize the local resources to allow water to be conveyed from its source to its destination where it will be fully utilized in irrigation. © 2013 Production and hosting by Elsevier B.V. on behalf of National Authority for Remote Sensing and Space Sciences. Source

Moustafa M.,Data Reception | Ebied H.M.,Ain Shams University | Helmy A.,Data Reception
Proceedings - 2013 8th International Conference on Computer Engineering and Systems, ICCES 2013 | Year: 2013

There is a high demand for high-resolution satellite sensing in modern application. Super Resolution (SR) offers an affordable solution for this high demand. The accuracy of super resolution depends on the accuracy of determining the difference between the low-resolution images. Shift estimation is the first and the most critical step in super resolution. This paper discusses shift estimation techniques in both spatial and frequency domains. It compares Vandewalle algorithm, Lucchese algorithm and Keran algorithm. Two real satellite images (SPOT-5) are used in the experiment. The images have -0.5 and 0.5 sub pixel shift in the horizontal and vertical directions respectively. The experimental results show that the Estimation shift parameters in spatial domain methods outperform the frequency domain methods. © 2013 IEEE. Source

Metwalli M.R.,Data Reception | Nasr A.H.,Data Reception | Faragallah O.S.,Menoufia University | El-Rabaie E.-S.M.,Menoufia University | And 5 more authors.
International Journal of Remote Sensing | Year: 2014

Recent studies show that hybrid panchromatic sharpening (pan-sharpening) methods using the non-sub-sampled contourlet transform (NSCT) and classical pan-sharpening methods such as intensity, hue and saturation (IHS), principal component analysis (PCA), and adaptive principal component analysis (APCA) reduce spectral distortion in pan-sharpened images. The NSCT is a shift-invariant multi-resolution decomposition. It is based on non-sub-sampled pyramid (NSP) decomposition and non-sub-sampled directional filter banks (NSDFBs). We compare the performance of the APCA-NSCT using different NSP filters, NSDFB filters, number of decomposition levels, and number of orientations in each level on SPOT 4 data with a spatial resolution ratio of 1:2, and Quickbird data with a spatial resolution ratio of 1:4. Experimental results show that the quality of pan-sharpening of remote-sensing images of different spatial resolution ratios using the APCA-NSCT method is affected by NSCT parameters. For the NSP, the 'maxflat' filters have the best quality, while the 'sk' filters give the best quality for the NSDFB. Changing the number of orientations at the same level of decomposition in the NSCT has a small effect on both the spectral and spatial qualities. The spectral and spatial qualities of pan-sharpened images mainly depend on the number of decomposition levels. Too few decomposition levels result in poor spatial quality, while excessive levels of decomposition result in poor spectral quality. Two levels of decomposition in the case of SPOT 4 data with a spatial resolution ratio of 1:2 achieve the best results. Also, three levels of decomposition in the case of QuickBird data with a spatial resolution ratio of 1:4 show the best results. © 2014 Taylor & Francis. Source

Nasr A.H.,Data Reception | Helmy A.K.,Data Reception
2011 Joint Urban Remote Sensing Event, JURSE 2011 - Proceedings | Year: 2011

In this paper, we propose a super-resolution (SR) reconstruction algorithm for Egyptsat-1 images. We recombine the lower resolution of Egyptsat-1 bands in order to obtain a super-resolution product. The algorithm is based on image fusion scheme using the multi-resolution decomposition. The fusion process is done in steerable wavelet domain using normalized convolution technique. We show the implementation of the proposed algorithm and how it can make significant spatial resolution improvements from 7.8 m to 4 m without amplifying the noise and allowing recognition of objects with size approaching its limiting spatial resolution. The experimental results and the comparative analyses using the Modulation Transfer Function (MTF) and other measures verify the usefulness and effectiveness of this algorithm. © 2011 IEEE. Source

Moustafa M.,Data Reception | Ebeid H.M.,Ain Shams University | Helmy A.,Data Reception | Nazamy T.M.,Ain Shams University | Tolba M.F.,Ain Shams University
2015 IEEE 7th International Conference on Intelligent Computing and Information Systems, ICICIS 2015 | Year: 2015

Single image super resolution (SISR) is the process that obtains a high resolution image from a single low resolution (LR) image by increasing the high frequency information and removing the degradation of the noise. Sparse representation of signal assumes linear combinations of a few atoms from a pre -specified dictionary. Sparse representation has been used successfully as a prior in signal reconstruction. Dictionary design is crucial for the success of reconstruction high resolution images. This paper evaluates the performance of dictionary design models in both mathematical and learning based models, it also compares the wavelet method, Haar method, DCT method, MOD method and K-SVD method. Various experiments are conducted using a real SPOT-4 satellite image. Experimental results demonstrate that the learning based approaches are very effective in increasing resolution and compare favorably to mathematical based approaches. © 2015 IEEE. Source

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