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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Merza, Hayder Mosa
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Computer Science & IT Engineering

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Enhancing aerial image registration: outlier filtering through feature classification Merza, Hayder Mosa; Sbeity, Ihab; Dbouk, Mohamed; Ibrahim, Zein Al Abidin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1900-1912

Abstract

In the context of feature-based image registration, the crucial task of outlier removal plays a pivotal role in achieving precise registration accuracy. This research introduces an innovative binary classifier founded on an adaptive approach for effectively identifying and eliminating outliers. The methodology begins with the utilization of the scale invariant feature transform (SIFT) to extract features from two images, initially matched using the Euclidian distance metrics. Subsequently, a classification procedure is executed to segregate the feature points into two categories: genuine matches (inliers) and spurious matches (outliers), which is accomplished through the brute-force matcher (BFM) technique. To enhance this process further, a novel classifier rooted in the random forest algorithm is introduced. This classifier is trained and tested using a comprehensive dataset curated for this study. The newly proposed classifier plays a pivotal role in attenuating the influence of outliers, ultimately leading to refined image registration process characterized by enhanced accuracy. The effectiveness of this outlier removal approach is assessed through a meticulous analysis of positional and classification accuracy. Additionally, we offer comparative insights by evaluating the performance of selected algorithm on our dataset.
Building change detection via classification in high-resolution aerial imagery Merza, Hayder Mosa; Sbeity, Ihab; Dbouk, Mohamed; Ibrahim, Zein Al-Abidin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4319-4331

Abstract

This research investigates the detection of changes in building structures within high-resolution aerial images of Baghdad, Iraq, over two years, 2007 and 2024. Employing advanced remote sensing techniques and sophisticated image processing algorithms, this study aims to identify and quantify alterations in the urban landscape accurately by addressing the key challenges inherent in the image registration process, as well as the availability associated with change detection (CD) techniques. We examined the data collection strategies, evaluated matching methods, and compared CD approaches. Aerial images were accurately analyzed to detect changes in building footprints, construction activities, and destruction. We developed a comprehensive annotation methodology tailored to the complex urban environment of Baghdad. These findings emphasize the rapidly evolving nature of Baghdad’s urban fabric and the critical need for ongoing monitoring to inform urban planning and management strategies. The results demonstrate the efficacy of utilizing high-resolution aerial imagery with object-based CD techniques for detailed urban analysis. This research advances the existing knowledge by providing a robust framework for urban CD, with implications for enhancing urban planning and policy-making processes. Future research will focus on refining the annotation processes and incorporating additional data sources to enhance the accuracy and comprehensiveness of urban CD methodologies.
Co-Authors Dbouk, Mohamed Ibrahim, Zein Al Abidin Ibrahim, Zein Al-Abidin Sbeity, Ihab