TY  - EJOU
AU  - Kamal, Mian Muhammad 
AU  - Abideen, Syed Zain Ul 
AU  - Al-Khasawneh, M. A. 
AU  - Momani, Alaa M. 
AU  - Mostafa, Hala 
AU  - Atoum, Mohammed Salem 
AU  - Ullah, Saeed 
AU  - Alsayaydeh, Jamil Abedalrahim Jamil 
AU  - Yusof, Mohd Faizal Bin 
AU  - Najib, Suhaila Binti Mohd 

TI  - Meyer Wavelet Transform and Jaccard Deep Q Net for Small Object Classification Using Multi-Modal Images
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2025
VL  - 144
IS  - 3
SN  - 1526-1506

AB  - 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.
KW  - Small object detection; multimodality; deep learning; jaccard deep Q-net; deep maxout network

DO  - 10.32604/cmes.2025.067430