Ayman H. Nasr

Image fusion based on principal component analysis and high-pass filter

Several commercial earth observation satellites carry dual-resolution sensors, which provide high spatial resolution panchromatic image and low spatial resolution multi-spectral image. Image fusion techniques are therefore useful for integrating a high spectral resolution image with a high spatial resolution image, to produce a fused image with high spectral and spatial resolutions. Some image fusion methods such as IHS, PC and BT provide superior visual high-resolution multi-spectral images but ignore the requirement of high-quality synthesis of spectral information. The high-quality synthesis of spectral information is very important for most remote sensing application based on spectral signatures, such as lithology, soil and vegetation analysis. Another family of image fusion techniques such as HPF operates on the basis of the injection of high-frequency components from the high spatial resolution panchromatic image into the multi-spectral image. This family of methods provides less spectral distortion. In this paper we propose to integrate between the two families PCA and HPF to provide pan sharpened image with superior spatial resolution and less spectral distortion. The experiments have shown that the proposed fusion method retains the spectral characteristics of the multi-spectral image and improves at the same time the spatial resolution of the fused image.

Spatial Cloud Detection and Retrieval System for Satellite Images

In last the decade we witnessed a large increase in data generated by earth observing satellites. Hence, intelligent processing of the huge amount of data received by hundreds of earth receiving stations, with specific satellite image oriented approaches, presents itself as a pressing need. One of the most important steps in earlier stages of satellite image processing is cloud detection. Satellite images having a large percentage of cloud cannot be used in further analysis. While there are many approaches that deal with different semantic meaning, there are rarely approaches that deal specifically with cloud detection and retrieval. In this paper we introduce a novel approach that spatially detect and retrieve clouds in satellite images using their unique properties .Our approach is developed as spatial cloud detection and retrieval system (SCDRS) that introduce a complete framework for specific semantic retrieval system. It uses a Query by polygon (QBP) paradigm for the content of interest instead of using the more conventional rectangular query by image approach. First, we extract features from the satellite images using multiple tile sizes using spatial and textural properties of cloud regions. Second, we retrieve our tiles using a parametric statistical approach within a multilevel refinement process. Our approach has been experimentally validated against the conventional ones yielding enhanced precision and recall rates in the same time it gives more precise detection of cloud coverage regions.

Efficient pan-sharpening of satellite images with the contourlet transform

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.

Sharpening Misrsat-1 data using Super-Resolution and HPF fusion methods

Spatial resolution enhancement is usually required in the remote sensing field. Super-Resolution (SR) is a fusion process for reconstructing a High-Resolution (HR) image from several Low-Resolution (LR) images covering the same region in the world. It is difficult, however, for some satellite remote sensing arrangements to get several images of the same scene in a short time, especially for highly dynamic scenes. In this paper, we study the SR process of Misrsat-1 data using sub-pixel shifts between bands 1, 3, and the Panchromatic (PAN) sub-band. Due to the difference in radiometry between the different bands, we propose performing the SR process between the high-pass details extracted from bands 1, 3, and the PAN, and then using the High-Pass Filter (HPF) fusion method for sharpening the Multi-Spectral (MS) image of Misrsat-1 using the super-resolved high-pass details. The comparison of the proposed method with the cubic convolution interpolation method has shown an enhancement in the image entropy, Point Spread Function (PSF), and Modulation Transfer Function (MTF).

Combining Superresolution and Fusion Methods for Sharpening Misrsat-1 Data

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.

Egyptsat-1 super-resolution image reconstruction using data fusion

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.

Use of intensity-hue-saturation (IHS) transformations in change detection of multitemporal remotely sensed data

A variety of techniques exist for change detection of multitemporal remotely sensed satellite data. The Intensity- Hue-Saturation (IHS) color space is very useful for image processing because it separates the color information in ways that correspond to the human visual systems response. In this study, a novel approach, emphasizing the use of the hue component of the IHS transformations of Landsat data, is proposed and examined for multitemporal change detection. Two Landsat Thematic Mapper (TM) scenes acquired on 1987 and 1997 covering the western part of El-Fayoum area and El- Rayan lakes in Egypt have been processed (geometrically corrected and radiometrically balanced) and transformed to the IHS space. The results of using the hue component in detecting the changes are very promising. A number of changed areas including water and agriculture land were successfully detected. The used color theme print, which display the spatial pattern of change in map form, was of great significance in interpreting the environmental changes and the statistical estimation of these changes has been carried out as well.

SUPER RESOLUTION FOR EGYPTSAT-1 IMAGES WITH ERRATIC SHIFT

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 cert ain malfunction, some Egyptsat-1 images have inconsistent sub-pixel shift. Therefore, in this study we pro pose a methodology to use this kind of shift for reconstru cting a SR image of Egyptsat-1 from its low resolut ion 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 reliab le shift. Then a SR method based on image fusion scheme with multi-resolution decomposition is performed. The fusion process is conducted in steerable wavelet do main using normalized convolution technique. It all ows the recognition of objects with size approaching its li miting spatial resolution. Results show that the pr oposed methods make significant spatial resolution improve ments from 7.8 to 4 m. Different quantitative measu res 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 pro jection. The visual and quantitative evaluations verify the usefulness and effectiveness of the proposed method ology.

Comparative Performance of the Integration of ETM-8 and ERS-1 Data for Geological Application

Multispectral optical data are sensitive to the physical properties of the ground objects and express their spectral features. While SAR data are more influenced by the geometric properties and express backscatter information. Therefore, this study demonstrates the integration of Landsat ETM-8 and ERS-1 data for improved information, more specific inferences and increased interpretation capabilities. Since SAR images are affected by speckle, some standard speckle reduction filters like Lee-Sigma, Frost, and GammaMap were compared. Our focus was on the impact of the fusion on enhancing subsurface features for geological exploration. The fusion was performed using different algorithms namely; Intensity–Hue–Saturation (IHS), Multiplicative Transform (MT), and Gram-Schmidt (GS). The experimental results showed complementary spatial and spectral resolution characteristics. The joint processing contains the details beneath the surface cover of the respective ERS-1 data while maintaining the basic color content of the original ETM-8 data. The fused images have potentially enhanced subsurface features such as structures, paleo drainage, several deposits, and reveals the fluvial features which are not observable in the ETM-8 image. In addition to the visual interpretation, the performance of each method was further quantitatively analyzed by applying the following three measures: The High Pass Correlation Coefficient (HPCC), the Root Mean Squared Error (RMSE) and the Structural Similarity Index Measure (SSIM) which depicted that the Gram-Schmidt (GS) method gives the best synthesized results and outperformed the other methods.

Comparative study for the DEM generation from RADARSAT stereoscopic data and topographic maps

Radar satellite images could be used to produce digital elevation model (DEM) of certain areas by processing a couple of images, covering the same area, obtained at two different angles. In this study, the DEM generated from the Canadian RADARSAT stereoscopic data for a north western area of the Gulf of Suez, Egypt, is compared to the DEM generated from the topographic contour maps, scale 1:50,000. An evaluation and assessment of the results were conducted. The study shows that the DEM derived from RADARSAT data has a high precision as compared to the one generated from the topographic maps. It is also accurate enough to provide information where other sources of digital elevation are not available.