We present algorithm evaluations for ATR of small sea vessels. The targets are at km distance from the sensors, which
means that the algorithms have to deal with images affected by turbulence and mirage phenomena. We evaluate
previously developed algorithms for registration of 3D-generating laser radar data. The evaluations indicate that some
robustness to turbulence and mirage induced uncertainties can be handled by our probabilistic-based registration method.
We also assess methods for target classification and target recognition on these new 3D data.
An algorithm for detecting moving vessels in infrared image sequences is presented; it is based on optical flow
estimation. Detection of moving target with an unknown spectral signature in a maritime environment is a challenging
problem due to camera motion, background clutter, turbulence and the presence of mirage. First, the optical flow caused
by the camera motion is eliminated by estimating the global flow in the image. Second, connected regions containing
significant motions that differ from camera motion is extracted. It is assumed that motion caused by a moving vessel is
more temporally stable than motion caused by mirage or turbulence. Furthermore, it is assumed that the motion caused
by the vessel is more homogenous with respect to both magnitude and orientation, than motion caused by mirage and
turbulence. Sufficiently large connected regions with a flow of acceptable magnitude and orientation are considered
target regions. The method is evaluated on newly collected sequences of SWIR and MWIR images, with varying targets,
target ranges and background clutter.
Finally we discuss a concept for combining passive and active imaging in an ATR process. The main steps are passive
imaging for target detection, active imaging for target/background segmentation and a fusion of passive and active
imaging for target recognition.