Maged Marghany

Exploration of gold mineralization in a tropical region using Earth Observing-1 (EO1) and JERS-1 SAR data: a case study from Bau gold field, Sarawak, Malaysia

Bau gold mining district, located near Kuching, Sarawak, Malaysia, is a Carlin style gold deposits. Geological analyses coupled with remote sensing data were used to detect hydrothermal alteration rocks and structure elements associated with this type of gold mineralization. Image processing techniques, including principal components analysis, linear spectral unmixing, and Laplacian algorithms, were employed to carry out spectrolithological–structural mapping of mineralized zones, using Advanced Land Imager, Hyperion, and JERS-1 synthetic aperture radar scenes covering the study area and surrounding terrain. Hydrothermally alteration mineral zones were detected along the SSW to NNE structural trend of the Tai Parit fault that corresponds to the areas of occurrence of the gold mineralization in the Bau limestone. The results show that potentially interesting areas are observable by the methods used, despite limited bedrock exposure in this region and the constraints imposed by the tropical environment.

Modification of fractal algorithm for oil spill detection from RADARSAT-1 SAR data

This paper introduces a modified formula for the fractal box counting dimension. The method is based on utilization of the probability distribution formula in the fractal box count. The purpose of this method is to use it for the discrimination of oil spill areas from the surrounding features, e.g., sea surface and look-alikes in RADARSAT-1 SAR Wide beam mode (W1) and Standard beam mode (S2) data have been collected under different wind speeds. The results show that the new formula of the fractal box counting dimension is able to discriminate between oil spills, look-alike areas and pixels of the size of a single ship. The W1 mode data illustrate an error standard deviation of 0.05, thus performing a better discrimination of oil spills as compared to S2 mode data. We conclude that automatic detection and discrimination of oil spill and other sea surface features can be opertionalized by using the new formula for fractal box counting.

3-D visualizations of coastal bathymetry by utilization of airborne TOPSAR polarized data

Abstract Multi-frequency C and L bands in the TOPSAR data have been utilized to reconstruct three-dimensional (3-D) bathymetry pattern. The main objective of this study is to utilize fuzzy arithmetic to reduce the errors arising from speckle in synthetic aperture radar (SAR) data when constructing ocean bathymetry from polarized SAR data. In doing so, two 3-D surface models, the Volterra algorithm and a fuzzy B-spline (FBS) algorithm, which construct a global topological structure between the data points, were used to support an approximation to the real surface. Volterra algorithm was used to express the non-linearity of TOPSAR data intensity gradient based on the action balance equation (ABC). In this context, a first-order kernel of Volterra algorithm was used to express ABC equation. The inverse of Volterra algorithm then performed to simulate 2-D current velocities from CVV and LHH band. Furthermore, the 2-D continuity equation then used to estimate the water depth. In order to reconstruct 3-D bathymetry pattern, the FBS has been performed to water depth information which was estimated from 2-D continuity equation. The best reconstruction of coastal bathymetry of the test site in Kuala Terengganu, Malaysia, was obtained with polarized L and C bands SAR acquired with HH and VV polarizations, respectively. With 10 m spatial resolution of TOPSAR data, bias of –0.004 m, the standard error mean of 0.023 m, r 2 value of 0.95, and 90% confidence intervals in depth determination was obtained with LHH band.

Comparison between radarsat-1 SAR different data modes for oil spill detection by a fractal box counting algorithm

Abstract This work presents a modified formula for the fractal box counting dimension. The method is based on the utilisation of the probability distribution formula in the fractal box count. The purpose of this method is to use it for the discrimination of oil spill areas from the surrounding features, e.g. sea surface and look-alikes, using RADARSAT-1 SAR Wide beam mode (W1), Standard beam mode (S2) and Standard beam mode (S1) data acquisition under different wind speeds. The results show that the new formula is able to discriminate between oil spills and look-alike areas. The results also illustrate that the new fractal formula identifies well the deficiency of oil spills in pairs of S2 data. Further, there are no significant differences between fractal values of look-alikes, low wind zone, and current shear features in different beam modes for acquisition of RADARSAT-1 SAR data. The W1 mode data, however, show an error standard deviation of 0.002, thus performing a better discrimination of oil spills than the S1 and S2 mode data.

Genetic Algorithm for Oil Spill Automatic Detection from Envisat Satellite Data

The merchant ship collided with a Malaysian oil tanker on May 25, 2010, and spilled 2,500 tons of crude oil into the Singapore Straits. The main objective of this work is to design automatic detection procedures for oil spill in synthetic aperture radar (SAR) satellite data. In doing so the genetic algorithm tool was designed to investigate the occurrence of oil spill in Malaysian coastal waters using ENVISAT ASAR satellite data. The study shows that crossover process, and the fitness function generated accurate pattern of oil slick in SAR data. This shown by 85% for oil spill, 5% look–alike and 10% for sea roughness using the receiver –operational characteristics (ROC) curve. It can therefore be concludedcrossover process, and the fitness function have the main role in genetic algorithm achievement for oil spill automatic detection in ENVISAT ASAR data.

Fractal dimension algorithm for detecting oil spills using RADARSAT-1 SAR

This paper introduces a method for modification of the formula of the fractal box counting dimension. The method is based on the utilization of the probability distribution formula in the fractal box count. The purpose of this method is to use it for the discrimination of oil spill areas from the surrounding features e.g., sea surface and look-alikes in RADARSAT-1 SAR data. The result shows that the new formula of the fractal box counting dimension is able to discriminate between oil spills and look-alike areas. The low wind area has the highest fractal dimension peak of 2.9, as compared to the oil slick and the surrounding rough sea. The maximum error standard deviation of low wind area is 0.68 which performs with fractal dimension value of 2.9.

Utilization of a genetic algorithm for the automatic detection of oil spill from RADARSAT-2 SAR satellite data

In this work, a genetic algorithm is applied for the automatic detection of oil spills. The procedure is implemented using sequences from RADARSAT-2 SAR ScanSAR Narrow single-beam data acquired in the Gulf of Mexico. The study demonstrates that the implementation of crossover allows for the generation of an accurate oil spill pattern. This conclusion is confirmed by the receiver-operating characteristic (ROC) curve. The ROC curve indicates that the existence of oil slick footprints can be identified using the area between the ROC curve and the no-discrimination line of 90%, which is greater than that of other surrounding environmental features. In conclusion, the genetic algorithm can be used as a tool for the automatic detection of oil spills, and the ScanSAR Narrow single-beam mode serves as an excellent sensor for oil spill detection and survey.

DInSAR technique for three-dimensional coastal spit simulation from radarsat-1 fine mode data

This work presents a new approach to 3D spit simulation using differential synthetic aperture interferometry (DInSAR). In doing so, conventional DInSAR procedures are implemented to three repeat passes of RADARSAT-1 SAR fine mode data (F1). Further, a new application of using fuzzy B-spline algorithm is implemented with phase unwrapping technique. The study shows that the performance of DInSAR method using fuzzy B-spline is better than the DInSAR technique, which is validated by the coefficient of determination r2 of 0.96, probability p of 0.002, and accuracy (RMSE) of ± 0.034 m, with 90% confidence intervals. In conclusion, integration of fuzzy B-spline with phase unwrapping produces an accurate 3D coastal geomorphology reconstruction.

Modelling sea surface salinity from MODIS satellite data

In this study, we investigate the relative ability of least square algorithm to retrieve sea surface salinity (SSS) from MODIS satellite data. We also examine with comprehensive comparison of the root mean square of bias the difference between second polynomial order algorithm and least square algorithm. Both the least squares algorithm and second polynomial order algorithm are used to retrieve the sea surface salinity (SSS) from multi MODIS bands data. Thus, the basic linear model has been solved by using second polynomial order algorithm and least square estimators. The accuracy of this work has been examined using the root mean square of bias of sea surface salinity retrieved from MODIS satellite data and the in situ measurements that are collected along the east coast of Peninsular Malaysia by using hydrolab instrument. The study shows comprehensive relationship between least square method and in situ SSS measurements with high r2 of 0.96 and RMS of bias value of ±0.37 psu. The second polynomial order algorithm, however, has lower performance as compared to least square algorithm. Thus, RMS of bias value of ± 7.34 psu has performed with second polynomial order algorithm. In conclusions, the least square algorithm can be used to retrieve SSS from MODIS satellite data.

3D bathymetry reconstruction from airborne Topsar polarized data

This paper introduces a new methods for three-dimensional(3D) ocean bathymetry reconstruction using Airborne TOPSAR Synthetic Aperture data. The new method is based on integration between Fuzzy B-spline and Volterra algorithm. Volterra algorithm is used to simulate the ocean surface current from TOPSAR data. Then, ocean surface current information used as input for continuity equation to estimate the water depths at different locations in TOPSAR data. This study shows that 3D ocean bathymetry can be reconstructed from TOPSAR data. The maximum water depth of 20 m can be captured from TOPSAR data.