Image segmentation plays a vital role in applications such as remote sensing. For this example,
remote sensing, aerial image segmentation is a special case of image segmentation. There are
some unique features of aerial images, like noise in natural landscapes, which need to be
addressed in order to obtain an optimal solution. Bushes and rocks are examples of landscape
features with diverse and variable pixel values that need to be distinguished by the segmentation
process. Smoothing filters present a common solution to address the problem of noise in images,
as does aerial image segmentation. There are several image segmentation techniques used for
aerial image segmentation. Some of these techniques are more sensitive to noise problems, and
are necessary to discriminate between different smoothing filters. In this thesis, a number of
different aspects of aerial image segmentation and their solutions are explained. In addition to
this, a novel smoothing filter is introduced and compared with other methods using different
segmentation techniques. Finally, all of the previous points are applied to a real world problem.
