Abstract
M.A.
The dissertation topic is the comparison of pixel-based and object-oriented image analysis approaches for camouflaged object detection research. A camouflage field trial experiment was conducted during 2004. For the experiment, 11 military vehicles were deployed along a tree line and in an open field. A subset of the vehicles was deployed with a variety of experimental camouflage nets and a final subset was left uncovered. The reason for deploying the camouflaged objects in the open without the use of camouflage principals was to create a baseline for future measurements. During the next experimental deployment, the camouflaged targets will be deployed according to camouflage principals. It must be emphasised that this is an experimental deployment and not an operational deployment.
Unobstructed entity panels were also deployed and served as calibration entities. During the trial, both airborne (colour aerial photography) and space borne (multi-spectral QuickBird) imagery were acquired over the trial sites, and extensive calibration and ground truthing activities were conducted in support of these acquisitions. This study further describes the processing that was done after acquisition of the datasets. The goal is to determine which classification techniques are the most effective in the detection of camouflaged objects. This will also show how well or poor the SANDF camouflage nets and paint potentially perform against air and space based sensors on the one hand and classification techniques on the other. Using this information, DPSS can identify the nets and paints that need to be investigated for future enhancements (e.g. colour selection, colour combinations, base material, camouflage patterns, entity shapes, entity textures, etc.). The classification techniques to be used against SANDF camouflaged objects will also give an indication of their performance against camouflaged advesarial forces in the future.