Meyer Wavelet Transform and Jaccard Deep Q Net for Small Object Classification Using Multi-Modal Images

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.