The work presented in this paper is based on a dataset recorded with an airborne sensor. It comprises targets like M-60,
M-47, M-113, bridge layers, tank retrievers, and trucks in various types of scenes.
The background-object segmentation consists of first estimating the ground level everywhere in the scene, and then for
each sample simply subtracting the measured height and ground level height. No assumptions concerning flat terrain etc.
Samples with height above ground level higher than a certain threshold are clustered by utilizing a straightforward
agglomerative clustering algorithm. Around each cluster the bounding box with minimum volume is determined. Based
on these bounding boxes, too small as well as too large clusters can easily be removed.
However, vehicle-sized clutter will not be removed. Clutter detection is based on estimating the normal vector for a
plane approximation around each sample. This approach is based on the fact that the surface normals of a vehicle is more
“modulo 90°” distributed than clutter.
The aim of the classification has been to classify main battle tanks (MBTs) Two types of algorithms have been studied,
one based on Dempster Shafer fusion theory, and one model based.
Our dataset comprises clusters of 269 vehicles (among them 131 MBTs), and 253 clutter objects (i.e. in practice vehiclesized
bushes). The experiments we have carried out show that the segmentation extracts all vehicles, the clutter detection
removes 90% of the clutter, and the classification finds more than 95% of the MBTs as well as removes half of the