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Moustafa M.,Data Reception | Ebied H.M.,Ain Shams University | Helmy A.,Data Reception | Nazamy T.M.,Ain Shams University | Tolba M.F.,Ain Shams University
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

Super Resolution (SR) is a technique to recover a high-resolution (HR) image from different noisy low resolution (LR) images. The missing highfrequency components in LR images should be restored correctly in HR image. Because of the extensive size of satellite images, the utilize to parallel algorithms can accomplish results more quickly with accurate results. This paper proposes an accelerated parallel implementation for an example based super-resolution algorithm, Neighbor Embedding (NE), using GPU. The NE trains the dictionary with patches obtained from a single image in the training phase. Euclidean distances are used to obtain the optimal weights that will be used in the construction of high-resolution images. Compute Device Unified Architecture (CUDA) by NVidia’s has been used to implement the proposed parallel NE. Different experiments have been carried out on a synthetic test image and satellite test image. The proposed GPU implementation of the NE was benchmarked against the serial implementation. The experimental results show that the speed of the implementation depends on the image size. The speed of the GPU implementation compared to the serial one using CPU ranged from 20× for small images to more than 30× for large image size. © Springer International Publishing Switzerland 2015.

Metwalli M.R.,Data Reception | Nasr A.H.,Data Reception | Allah O.S.F.,Menoufia University | El-Rabaie S.,Menoufia University | Abd El-Samie F.E.,Menoufia University
Journal of the Optical Society of America A: Optics and Image Science, and Vision | Year: 2010

This paper presents an integrated method for the fusion of satellite images. Several commercial earth observation satellites carry dual-resolution sensors, which provide high spatial resolution or simply high-resolution (HR) panchromatic (pan) images and low-resolution (LR) multi-spectral (MS) images. Image fusion methods are therefore required to integrate a high-spectral- resolution MS image with a high-spatial-resolution pan image to produce a pan-sharpened image with high spectral and spatial resolutions. Some image fusion methods such as the intensity, hue, and saturation (IHS) method, the principal component analysis (PCA) method, and the Brovey transform (BT) method provide HR MS images, but with low spectral quality. Another family of image fusion methods, such as the high-pass-filtering (HPF) method, operates on the basis of the injection of high frequency components from the HR pan image into the MS image. This family of methods provides less spectral distortion. In this paper, we propose the integration of the PCA method and the HPF method to provide a pan-sharpened MS image with superior spatial resolution and less spectral distortion. The experimental results show that the proposed fusion method retains the spectral characteristics of the MS image and, at the same time, improves the spatial resolution of the pan-sharpened image. © 2010 Optical Society of America.

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.

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.

Moustafa M.,Data Reception | Ebeid H.M.,Ain Shams University | Helmy A.,Data Reception | Nazmy T.M.,Ain Shams University | Tolba M.F.,Ain Shams University
International Journal of Remote Sensing | Year: 2016

Recently, compressive sensing (CS) has offered a new framework whereby a signal can be recovered from a small number of noisy non-adaptive samples. This is now an active area of research in many image-processing applications, especially super-resolution. CS algorithms are widely known to be computationally expensive. This paper studies a real time super-resolution reconstruction method based on the compressive sampling matching pursuit (CoSaMP) algorithm for hyperspectral images. CoSaMP is an iterative compressive sensing method based on the orthogonal matching pursuit (OMP). Multi-spectral images record enormous volumes of data that are required in practical modern remote-sensing applications. A proposed implementation based on the graphical processing unit (GPU) has been developed for CoSaMP using computed unified device architecture (CUDA) and the cuBLAS library. The CoSaMP algorithm is divided into interdependent parts with respect to complexity and potential for parallelization. The proposed implementation is evaluated in terms of reconstruction error for different state-of-the-art super-resolution methods. Various experiments were conducted using real hyperspectral images collected by Earth Observing-1 (EO-1), and experimental results demonstrate the speeding up of the proposed GPU implementation and compare it to the sequential CPU implementation and state-of-the-art techniques. The speeding up of the GPU-based implementation is up to approximately 70 times faster than the corresponding optimized CPU. © 2016 Informa UK Limited, trading as Taylor & Francis Group.

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.

Metwalli M.R.,Data Reception | Metwalli M.R.,Menoufia University | Nasr A.H.,Data Reception | Faragallah O.S.,Menoufia University | And 2 more authors.
IEEE Transactions on Geoscience and Remote Sensing | Year: 2013

This paper presents an efficient technique for sharpening of Misrsat-1 data using superresolution (SR) methods and fusion methods. Due to the difference in spectral characteristics between bands 1 and 3 and the panchromatic (PAN) band of Misrsat-1, we implement SR on high details of these bands and use the resulting image to sharpen the bands of the multispectral (MS) image. Several SR methods are tested and compared in this paper for this purpose. The first class of methods uses spatial-domain SR, in which SR is performed on the high-pass details extracted from bands 1 and 3 and the PAN band. The superresolved high-pass details are used after that to enhance the spatial resolution of the MS data using the high-pass filter fusion method. The second class of methods depends on the interpolation of coefficients in the high-frequency subbands of a multiscale representation of bands 1 and 3 and the PAN band and an additive fusion method to add the high-frequency subband coefficients to different bands of the MS image. A comparison study between different SR methods belonging to the aforementioned classes such as nonuniform interpolation (NUI), projection onto convex sets (POCS), iterative back projection (IBP), structure-adaptive normalized convolution (SANC), and adaptive steering kernel regression (ASKR) is presented. The simulation results show that iterative SR methods such as IBP and POCS produce more noise than interpolation methods such as NUI, SANC, and ASKR. The results also reveal that combining the ASKR with a multiscale decomposition enhances the signal-to-noise ratio. © 1980-2012 IEEE.

Zeyada H.H.,Data Reception | Ezz M.M.,Al - Azhar University of Egypt | Nasr A.H.,Data Reception | Shokr M.,Data Reception | Harb H.M.,Al - Azhar University of Egypt
International Journal of Remote Sensing | Year: 2016

Ground cover classification is one of the core applications in remote sensing. Classification of imaging radar data using conventional single-channel or dual-channel usually results in poor accuracy due to limited number of observations. For this reason, applications of full polarimetric data are growing. In this article, Radarsat-2 quad-pol data of an agriculture scene acquired on August, 2013 over the Nile Delta, Egypt, has been used to evaluate the accuracy of three supervised classification methods, Support Vector Machine (SVM), multilayer perceptron (MLP), and decision tree (DT), in discriminating between four different crops: rice, maize, grape and cotton, in addition to urban land cover. A set of principal components was used in the SVM classifier. Our goal is to build a generic model that minimizes the error of future classification using the same training data. Results indicate that combining the most commonly used polarimetric parameters from Pauli, Cloude–Pottier, and Freeman–Durden decompositions along with the three fundamental backscatter coefficients (σ0 hh,σ0 vv, and σ0 hv) using SVM classifier strike an appropriate balance that minimizes the effect of the training error on the classification accuracy without going to over-fitting classification. This leads to the highest overall classification accuracy of 94.48%. The overall classification accuracy using different combinations of parameters and different classifiers is given. © 2016 Informa UK Limited, trading as Taylor & Francis Group.

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.

Nasr A.H.,Data Reception | El-Tawel G.S.,Suez Canal University | Helmy A.K.,Data Reception
Journal of Computer Science | Year: 2014

The key point of the Super-Resolution (SR) process is the accurate registration of the low resolution images, i.e., accurate measuring of the fixed shift between them, to obtain high resolution image. Due to certain malfunction, some Egyptsat-1 images have inconsistent sub-pixel shift. Therefore, in this study we propose a methodology to use this kind of shift for reconstructing a SR image of Egyptsat-1 from its low resolution bands. It is a trade-off between the capability of catching spatial details and the sensitivity to the erratic shift existed along the image. Firstly, this inconsistent shift between the bands is transformed into reliable shift. Then a SR method based on image fusion scheme with multi-resolution decomposition is performed. The fusion process is conducted in steerable wavelet domain using normalized convolution technique. It allows the recognition of objects with size approaching its limiting spatial resolution. Results show that the proposed methods make significant spatial resolution improvements from 7.8 to 4 m. Different quantitative measures of the proposed methodology were assessed and tested with some implemented commonly used SR methods. These methods are; nonparametric bayesian, POCS, iterative-interpolation, robust and iterated back projection. The visual and quantitative evaluations verify the usefulness and effectiveness of the proposed methodology. © 2014 Science Publications.

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