Multi-sensor image fusion for target recognition in the environment of network decision support systems

Abstract

This thesis proposed a concept of distributed management of littoral operations at the tactical level, in which timeliness of information and reduced decision cycles are of critical importance. The use of mesh tactical networks augmented by sensor management, operational databases, and an appropriate level of automation of target recognition can turn the obstacles of land masses in littoral environments into a tactical advantage. Ultimately, this thesis concept aimed to enhance situational awareness by enabling the timely exploitation and dissemination of imagery data from small satellites and unmanned systems at the tactical level. Analyses of simulation and field experimentation results that focused on mobile ad-hoc networks (MANETs)—which connected dissimilar imaging sensors and enabled fusion of captured images—supported this concept. Mesh tactical radios provided an adequate range and quality of service (QoS) to enable networking of kinetic and non-kinetic assets equipped with imaging or data relaying capabilities and to support dissemination of imagery data. Additionally, multi-spectral image fusion of thermal and visual images for target recognition yielded the best classification performance after the use of speeded-up robust features (SURF) and artificial neural networks (ANNs).