Efficient military operations require insight in the capabilities of the available sensor package to reliably assess the
operational theatre, as well as insight in the adversary's capabilities to do the same. This paper presents the EOSTAR
model suite, an end-to-end approach to assess the performance of electro-optical sensor systems in an operational
setting. EOSTAR provides the user with coverage diagrams ("where can I see the threat?") and synthetic sensor images
("how do I perceive the threat?"), and allows assessing similar parameters for threat sensors. The paper discusses the
elements of EOSTAR and outlines a few of the possible applications of the model.
When bright moving objects are viewed with an electro-optical system at very long range, they will appear as
small slightly blurred moving points in the recorded image sequence. Detection of point targets is seriously
hampered by structure in the background, temporal noise and aliasing artifacts due to undersampling by the
infrared (IR) sensor.
Usually, the first step of point target detection is to suppress the clutter of the stationary background in the
image. This clutter suppression step should remove the information of the static background while preserving
the target signal energy. Recently we proposed to use super-resolution reconstruction (SR) in the background
suppression step. This has three advantages: a better prediction of the aliasing contribution allows a better
clutter reduction, the resulting temporal noise is lower and the point target energy is better preserved.
In this paper the performance of the point target detection based on super-resolution reconstruction (SR)
is evaluated. We compare the use of robust versus non robust SR reconstruction and evaluate the effect of
regularization. Both of these effects are influenced by the number of frames used for the SR reconstruction and
the apparent motion of the point target. We found that SR improves the detection efficiency, that robust SR
outperforms non-robust SR, and that regularization decreases the detection performance. Therefore, for point
target detection one can best use a robust SR algorithm with little or no regularization.
In modern warfare scenarios naval ships must operate in coastal environments. These complex environments, in bays and narrow straits, with cluttered littoral backgrounds and many civilian ships may contain asymmetric threats of fast targets, such as rhibs, cabin boats and jet-skis. Optical sensors, in combination with image enhancement and automatic detection, assist an operator to reduce the response time, which is crucial for the protection of the naval and land-based supporting forces. In this paper, we present our work on automatic detection of small surface targets which includes multi-scale horizon detection and robust estimation of the background intensity. To evaluate the performance of our detection technology, data was recorded with both infrared and visual-light cameras in a coastal zone and in a harbor environment. During these trials multiple small targets were used. Results of this evaluation are shown in this paper.
When bright moving objects are viewed with an electro-optical system at long range, they appear as small, slightly blurred moving points in the recorded image sequence. Typically, such point targets need to be detected in an early stage. However, in some scenarios the background of a scene may contain much structure, which makes it difficult to detect a point target. The novelty of this work is that superresolution reconstruction is used for suppression of the background. With superresolution reconstruction a high-resolution estimate of the background, without aliasing artifacts due to undersampling, is obtained. After applying a camera model and subtraction, this will result in difference images containing only the point target and temporal noise. In our experiments, based on realistic scenarios, the detection performance, after background suppression using superresolution reconstruction, is compared with the detection performance of a common background suppression method. It is shown that using the proposed method, for an equal detection-to-false-alarm ratio, the signal strength of a point target can be up to 4 times smaller. This implies that a point target can be detected at a longer range.
In harbour environments operators should perform tasks as detection and classification. Present-day threats of small
objects, as jet skis etc, should be detected, classified and recognized. Furthermore threat intention should be analysed.
As harbour environments contain several hiding spaces, due to fixed and floating neutral objects, correct assessment of
the threats is complicated when detection tracks are intermittently known. For this purpose we have analysed the
capability of our image enhancement and detection technology to assess the performance of the algorithms in a harbour
environment. Data were recorded in a warm harbour location. During these trials several small surfaces targets were
used, that were equipped with ground truth equipment. In these environments short-range detection is mandatory,
followed by immediate classification. Results of image enhancement and detection are shown. An analysis was made
into the performance assessment of the detection algorithms.
Surveillance applications are primarily concerned with detection of targets. In electro-optical surveillance
systems, missiles or other weapons coming towards you are observed as moving points. Typically, such moving
targets need to be detected in a very short time. One of the problems is that the targets will have a low
signal-to-noise ratio with respect to the background, and that the background can be severely cluttered like
in an air-to-ground scenario.
The first step in detection of point targets is to suppress the background. The novelty of this work is
that a super-resolution reconstruction algorithm is used in the background suppression step. It is well-known
that super-resolution reconstruction reduces the aliasing in the image. This anti-aliasing is used to model
the specific aliasing contribution in the camera image, which results in a better estimate of the clutter in
the background. Using super-resolution reconstruction also reduces the temporal noise, thus providing a
better signal-to-noise ratio than the camera images. After the background suppression step common detection
algorithms such as thresholding or track-before-detect can be used.
Experimental results are given which show that the use of super-resolution reconstruction significantly
increases the sensitivity of the point target detection. The detection of the point targets is increased by the
noise reduction property of the super-resolution reconstruction algorithm. The background suppression is
improved by the anti-aliasing.
In tactical sensor imagery there always is a need for less noise, higher dynamic range and more resolution. Although recent developments lead to better and better Focal Plane Array (FPA) camera systems, modern infrared FPA camera system are still hindered by
non-uniformities, a limited signal-to-noise ratio and a limited spatial resolution. The current availability of fast and inexpensive digital electronics allows the use of advanced real-time signal processing to address the need for better image quality. We will present results of signal-conditioning algorithms, which achieve significant better performance with regard to the FPA problems given above. Scene-Based Non-Uniformity Correction (SBNUC) can provide an on-line correction of existing and evolving fixed-pattern noise. Dynamic Super Resolution (DSR) improves the signal-to-noise ratio, while simultaneously improving spatial resolution. The signal-conditioning algorithms can handle camera movements, high temporal noise levels, high fixed-pattern noise levels and large moving objects. The Local Adaptive Contrast Enhancement (LACE) algorithm does effectively compress the 10, 12 or 14 bits dynamic range of the corrected imagery towards a 6 to 8 bits dynamic range for the display system, without the loss of image details. In this process, it aims at keeping all information in the original image visible. We will show that the SBNUC, DSR, mosaic generation, and LACE can be integrated in a very natural way resulting in excellent all-round performance of the signal-conditioning suite. We will demonstrate the application of SBNUC, DSR, Mosaicking and LACE for various imaging systems, showing significant improvement of the image quality for several imaging conditions.
A combination of algorithms has been developed for the detection, tracking, and classification of targets at sea. In a flexible software setup, different methods of preprocessing and detection can be chosen for the processing of infrared and visible-light images. Two projects, in which the software is used, are discussed. In the SURFER project, the algorithms are used for the detection and classification of small targets, e.g., swimmers, dinghies, speedboats, and floating mines. Different detection methods are applied to recorded data. We will present a method to describe the background by fitting continuous functions to the data, and show that this provides a better separation between objects and clutter. The detection of targets using electro- optical systems is one part of this project, in which also algorithms for fusion of electro-optical data with radar data are being developed. In the second project, a simple infrared image-seeker has been built that is used to test the effectiveness of infrared decoys launched from a ship. In a more complicated image seeker algorithm, features such as contrast and size and characterization of trajectory are used to differentiate between ship, infrared decoys and false alarms resulting from clutter. In this paper, results for the detection of small targets in a sea background are shown for a number of detection methods. Further, a description is given of the simulator imaging seeker, and some results of the imaging seeker software applied to simulated and recorded data will be shown.
An algorithm is under development, which is based on the Triangle Orientation Discrimination (TOD)-method and predicts the characterization by human observers of camera- system performances. The algorithm combines the TOD-method, an early-vision model, and an orientation discriminator. The algorithm uses the same images as used in human-observer experiments. After correction for the physical properties of the display and the human eye, the algorithm tries to find the orientation of the stimulus. The algorithm can also predict the performance of only image processing using a simple scene-generator instead of a camera setup.