Enhanced situation awareness through CNN-based deep multimodal image fusion
Shuo Liu, Huan Liu, Vijay John, Zheng Liu, Erik Blasch
Abstract
Automated situation awareness (ASA) in a complex and dynamic setting is a challenging task. The accurate perception of environmental elements and events is critical for the successful completion of a mission. The key technology to implement ASA is target detection. However, in most situations, targets of interest that are at a distance are hard to identify due to the small size, complex background, and poor illumination conditions. Thus, multimodal (e.g., visible and thermal) imaging and fusion techniques are adopted to enhance the capability for situation awareness. A deep multimodal image fusion (DIF) framework is proposed to detect the target by fusing the complementary information from multimodal images with a deep convolutional neural network. The DIF is built and validated with the Military Sensing Information Analysis Center dataset. Extensive experiments were carried out to demonstrate the effectiveness and superiority of the proposed method in terms of both detection accuracy and computational efficiency.