While using unmanned systems in combat is not new, what will be new in the foreseeable future is how such systems are used and integrated in the civilian space. The potential use of Unmanned Aerial Vehicles in civil and commercial applications is becoming a fact, and is receiving considerable attention by industry and the research community. The majority of Unmanned Aerial Vehicles performing civilian tasks are restricted to flying only in segregated space, that are typically not above populated areas which in turn are one of the areas most useful for civilian applications. The reasoning behind the current restrictions is mainly due to the fact that current UAV technologies are not able to demonstrate an equivalent level of safety to manned aircraft, particularly in the case of an engine failure which would require an emergency or forced landing. Piloted aircraft in the same scenario have a human on board that is able to engage in the complex decision making process involved in the choice of a suitable landing location. If UAVs are to ever fly routinely in civilian airspace, then it is argued that the problem of finding a safe landing location for a forced landing is an important unresolved problem that must be addressed. This thesis presents the results of an investigation into the feasibility of using machine vision and image processing techniques to locate candidate landing sites for an autonomous UAV forced landing. To this end, some areas are extracted from aerial images which are suitable for UAV landing and others are reported as unsuitable or unknown areas. Aerial image segmentation is the main part of this thesis. Defining the surface type of each segment is also necessary. Recently, Active Contours and their implementation by Level Set method have received a great deal of attention within the image community, especially as a framework for dealing with image partitioning issues. This method allows introducing different kinds of information as constraints (texture features, color features, etc) to image partitioning process. A new model for active contours called region-based active contour (Chan-Vese model) has been proposed to detect objects in a given image, which is more powerful than inter-ideograph; TEXT-ALIGN: justify; LINE-HEIGHT: normal; MARGIN: 0cm 0cm 0pt; mso-layout-grid-align: none" Kay Words aerial image segmentation, features extraction, Active Contours, surface type classification, Unmanned Arial Vehicles (UAVs), emergency or forced landing
