Hasimah Ali
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Preferred name
Hasimah Ali
Official Name
Ali, Hasimah
Alternative Name
Ali, H.
Ali, H
Ali, Hashimah
Ali, H. I.
Bt Ali, Hasimah
Main Affiliation
Scopus Author ID
57218540740
Researcher ID
EKZ-6160-2022

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A cascade hyperbolic recognition of buried objects using hybrid feature extraction in ground penetrating radar images

2021-08-27 , Hasimah Ali , Ahmad Firdaus Ahmad Zaidi , Wan Khairunizam Wan Ahmad , Mohd Shuhanaz Zanar Azalan , Tengku Amran T.S. , Ahmad M.R. , Mohamed Elshaikh Elobaid Said Ahmed

Ground penetrating radar (GPR) has been acknowledged as effective nondestructive technique for imaging the subsurface. But the process of recognizing hyperbolic pattern of buried objects is subjective and mainly relies upon operator's knowledge and experience. This project proposed a hyperbolic recognition of buried objects using hybrid feature extraction in GPR subsurface mapping. In this framework, a cascade hyperbolic recognition by means of Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) are used as hybrid feature recognizing hyperbolic of buried objects. The rationale for an initial focus on cascade hyperbolic recognition is motivated by unique features exhibits by EMD and DWT behaviour in characterizing the hyperbolic pattern which make them particularly well suited to utilities detection in GPR. A series of experiments has been conducted on hyperbolic pattern based on hybrid features using four different geometrical shapes of cubic, cylindrical disc and spherical. Based on the results obtained, the hybrid features of IMF1+ wavelet transform (cH1) shows promising recognition rate in recognizing the hyperbolic that having different geometrical shapes of buried objects.

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Reconstruction hyperbola signature of underground object using GPR images for mapping applications

2024-02-08 , Masuan N.A. , Hasimah Ali , Amran T.S.T. , Ahmad Firdaus Ahmad Zaidi , Kamarudin K. , Ahmad M.R.

Ground penetrating radar has been acknowledged as an effective and efficient technique for non-destructive investigation for near-subsurface exploration that is based on the reflection receiver-transmitter of the antenna when hitting buried objects. An accurate interpretation of GPR data is greatly important in locating and mapping underground objects. Although GPR research has achieved remarkable success, the interpretation of GPR raw data highly depends on the reliance of user experts. Further, unexperienced GPR users are subject to error since the hyperbola signatures may resemble each other. Therefore, this work focuses on the development of a 3D reconstruction of the hyperbola signature of underground objects using GPR images for mapping applications. In this study, 3D reconstruction has been developed based on the Synthetic Aperture Focusing Technique, also known as SAFT. At the first stage, the raw input of GPR images was subjected to zero-time correction and background elimination. Next is the projection of each hyperbola signature by means of B-Scan images to create a 3D image. Then, the resultant 3D images were stacked together, and further 3D interpolation techniques were employed on the images. The experimental studies have been done on GPR data using a metal sphere as a sample. The findings of the study highlight that the SAFT method was able to reconstruct the 3D model of the hyperbola signature and exhibit the ability to provide clues about the location of the underground object through the representation of the voxel point of the images. Based on these results, the SAFT technique provides good insight into the 3D reconstruction of hyperbola signatures using GPR images in mapping applications.

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Rebar Path Mapping using Ground Penetrating Radar

2023-01-01 , Basri N.A.B. , Hasimah Ali , Ahmad M.R. , Ahmad Firdaus Ahmad Zaidi , Siti Nurul Aqmariah Mohd Kanafiah , Jusman Y.

Ground penetrating radar (GPR) is a non-destructive device that helps to determine the position and direction of underground utilities such as rebar while preventing any inaccurate excavation process. The direction of buried rebar is usually mapped using the X-Y grid scanning method, which requires a lot of manpower and time to complete. Therefore, this paper investigated the ability of parallel scanning of B-scan to imitate the result of C-scan produced by X-Y grid scanning. Parallel scanning has been emphasised to reduce the time consumption of the data acquisition process while delivering a quality output. To develop a rebar path mapping, a data processing step has been implemented on the B-scan data for seven parallel lines that correspond to the x-axis. Next, Kirchhoff migration has been applied along with stacking and interpolation techniques to map a two-dimensional (2-D) image of the buried rebar. The obtained result was then compared with the grid scanning data of C-scan to evaluate the correlation between them. The performance of the mapped rebar path using parallel B-scan data was evaluated based on the ability of the data to give an accurate depth calculation of the buried rebar. Ultimately, the results show that this proposed method for using parallel B-scan to do mapping is verified.

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Feature Extraction based on Empirical Mode Decomposition for Shapes Recognition of Buried Objects by Ground Penetrating Radar

2021-06-11 , Hasimah Ali , Mohd Shuhanaz Zanar Azalan , Zaidi A.F.A. , Amran T.S.T. , Ahmad M.R. , Elshaikh M.

Ground penetrating radar (GPR) is one of the promising non-destructive imaging tools investigations for shallow subsurface exploration such as locating and mapping the buried utilities. In practical applications, GPR images could be noisy due to the system noise, the heterogeneity of the medium, and mutual wave interactions thus, it is a complex task to recognizing the hyperbolic signature of buried objects from GPR images. Therefore, this paper aims to develop nonlinear feature extraction technique of using Empirical Mode Decomposition (EMD) in recognizing the four geometrical shapes (cubic, cylindrical, disc and spherical) from GPR images. A pre-processing step of isolating hyperbolic signature from different background was first employed by mean of Region of Interest (ROI). The hyperbolic signature that describes the shapes was extracted using EMD decomposition to obtain a set of significant features. In this framework, the hyperbolic pattern was decomposed of using EMD, to produce a small set of intrinsic mode functions (IMF) via sifting process. The IMF properties of the signature that exhibit the unique pattern was used as potential features to differentiate the geometrical shapes of buried objects. The extracted IMF features were then fed into machine learning classifier namely Support Vector Machines. To evaluate the effectiveness of the proposed method, a set data collection of GPR-images has been acquired. The experimental results show that the recognition rate of using IMF features was achieved 99.12% accuracy in recognizing the shapes of buried objects whose shows the promising result.

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Ground penetrating radar for buried utilities detection and mapping: a review

2021-12-01 , Hasimah Ali , Ideris N.S.M. , Ahmad Firdaus Ahmad Zaidi , Mohd Shuhanaz Zanar Azalan , Amran T.S.T. , Ahmad M.R. , Rahim N.A. , Shazmin Aniza Abdul Shukor

This paper presents a review on Ground Penetrating Radar (GPR) detection and mapping of buried utilities which have been widely used as non-destructive investigation and efficiently in terms of usage. The reviews cover on experimental design in GPR data collection and survey, pre-processing, extracting hyperbolic feature using image processing and machine learning techniques. Some of the issues and challenges facing by the GPR interpretation particularly in extracting the hyperbolas pattern of underground utilities have also been highlighted.

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Hyperbola detection of ground penetrating radar using deep learning

2024-02-08 , Zahir N.H.M. , Hasimah Ali , Nasri M.I.S. , Masuan N.A. , Zaidi A.F.A. , Mohd Shuhanaz Zanar Azalan , Amin M.S.M. , Ahmad M.R. , Elshaikh M.

Ground Penetrating Radar (GPR) is a geophysical method using high resolution electromagnetic used to acquire the information of underground. The electromagnetic (EM) waves produces from the antenna consisting of transmitter and receiver. The waves from the transmitter penetrates into the ground and reflect backs to the surface that receive by the antenna receiver. The antenna can lie within the range of 10MHz to 1000MHz to determine the shallow or deep penetration. Higher value of antenna will result in shallow penetration and otherwise for lower antenna. The process of recognition of buried objects is challenging task especially in the construction area to ensure safety and the quality of civil building. The GPR will display the mapping image on its control unit screen. If there are objects underground have detected, the image will display the hyperbola shape to indicate the target of the object. A vast number of data makes it difficult to classify each and every one of it either the image data is in which classes or categories. If there are many hyperbola present in image also makes it difficult to locate the accurate position. Due to this, deep learning technique by means of ResNet50 has been used in this research for hyperbola recognition in GPR image. A series of experiments has been conducted on the GPR dataset collected at Agency Nuclear Malaysia. Based on the results obtained, the deep learning model successfully learn the image feature. The accuracy of the model classified for this GPR data using ResNet50 gives 90% accuracy. Therefore, the proposed method for image recognition shows the promising results with all the GPR images are correctly recognize. Further, region of interest of hyperbola signature has been represented by a rectangular box indicates the hyperbola location

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Recognition of different utility pipes size of ground penetrating radar images at different penetration depth

2024-02-08 , Nasri M.I.S. , Hasimah Ali , Zaidi A.F.A. , Shukor S.A.A. , Ahmad M.R. , Amran T.S.T. , Siti Nurul Aqmariah Mohd Kanafiah , Othman S.M. , Elshaikh M.

Ground Penetrating Radar (GPR) is a geophysical locating method that uses radio waves to capture images below the surface of the ground in a minimally invasive way. It also requires two main essential equipment which is a transmitter and a receiving antenna. To address the problem, this project proposed the hyperbolic recognition of different utility pipes of GPR images at different level of penetration depth. In this framework, the raw data of GPR images were firstly to be pre-processed. The grayscale images were cropped, resized, and enhanced to increase the contrast of the features of the image. Then, the pre-processed GPR images were extracted using the Histogram of Oriented Gradient (HOG) method with three different windows. The extracted HOG features were then used as input to the k-Nearest Neighbor classifier. A series of experiments has been conducted using 10-fold cross-validation technique for training and testing the GPR data. Based on the result obtained, it shows that at depth 20cm the average accuracy is about 99.87%, whereas at depth 40cm the average accuracy achieved 100%. Thus, the result shows that the extracted HOG features exhibit the significant information of hyperbolic signature of different pipe size with different depth of buried object. Therefore the results seem promising in recognizing the hyperbolic of utilities.

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