Meyer wavelet transform and Jaccard Deep Q net for small object classification using multi-modal images

Muhammad Kamal, Mian and Jamil Alsayaydeh, Jamil Abedalrahim and Ul Abideen, Syed Zain and Al-Khasawneh, Mahmoud Ahmad and M. Momani, Alaa and Mostafa, Hala and Atoum, Mohammed Salem and Ullah, Saeed and Yusof, Mohd Faizal and Mohd Najib, Suhaila (2025) Meyer wavelet transform and Jaccard Deep Q net for small object classification using multi-modal images. Computer Modeling in Engineering and Sciences, 144 (3). pp. 3053-3083. ISSN 1526-1492

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Abstract

Accurate detection of small objects is critically important in high-stakes applications such as military reconnaissance and emergency rescue. However, low resolution, occlusion, and background interference make small object detection a complex and demanding task. One effective approach to overcome these issues is the integration of multimodal image data to enhance detection capabilities. This paper proposes a novel small object detection method that utilizes three types of multimodal image combinations, such as Hyperspectral–Multispectral (HS-MS), Hyperspectral–Synthetic Aperture Radar (HS-SAR), and HS-SAR–Digital Surface Model (HS-SAR-DSM). The detection process is done by the proposed Jaccard Deep Q-Net (JDQN), which integrates the Jaccard similarity measure with a Deep Q-Network (DQN) using regression modeling. To produce the final output, a Deep Maxout Network (DMN) is employed to fuse the detection results obtained from each modality. The effectiveness of the proposed JDQN is validated using performance metrics, such as accuracy, Mean Squared Error (MSE), precision, and Root Mean Squared Error (RMSE). Experimental results demonstrate that the proposed JDQN method outperforms existing approaches, achieving the highest accuracy of 0.907, a precision of 0.904, the lowest normalized MSE of 0.279, and a normalized RMSE of 0.528.

Item Type:	Article
Uncontrolled Keywords:	Small object detection, Multimodality, Deep learning, Jaccard deep Q-net, Deep Maxout Network
Divisions:	Faculty Of Electronics And Computer Technology And Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	17 Jul 2026 03:52
Last Modified:	17 Jul 2026 03:52
URI:	http://eprints.utem.edu.my/id/eprint/29975
Statistic Details:	View Download Statistic

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