Multi-sensor Image Fusion for Target Recognition in the Environment of Network Decision Support Syst

LCDR Michail Pothitos, Hellenic Navy

This thesis proposes a concept of distributed management of littoral operations at the tactical level, where 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.Analysis of simulation and field experimentation results focused on mobile ad-hoc networks (MANETs), which connect dissimilar imaging sensors and enable fusion of captured images, support this concept. The mesh tactical radios provide 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 support dissemination of imagery data. Additionally, multi-spectral image fusion of thermal and visual images for target recognition yields the best classification performance after the use of speed-up robust features (SURF), and artificial neural networks (ANNs). Ultimately, this concept aims to enhance situational awareness (SA) by enabling the timely exploitation and dissemination of imagery data from small satellites and unmanned systems at the tactical level.

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Jan 10, 2016

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