Multimodal image fusion for enhanced vehicle identification in intelligent transport.

ABSTRACT: Target detection in remote sensing is essential for applications such as law enforcement, military surveillance, and search-and-rescue. With advancements in computational power, deep learning methods have excelled in processing unimodal aerial imagery. The availability of diverse imaging modalities including, infrared, hyperspectral, multispectral, synthetic aperture radar, and Light Detection and Ranging (LiDAR) allows researchers to leverage complementary data sources. Integrating these multi-modal datasets has significantly enhanced detection performance, making these technologies more effective in real-world scenarios. In this work, we propose a novel approach that employs a deep learning-based attention mechanism to generate depth maps from aerial images. These depth maps are fused wi

SUBMITTER: Mudawi NA 

PROVIDER: S-EPMC12594218 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature