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.

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 signi£cant 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 £xed-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 £xed-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 signalconditioning suite. We will demonstrate the application of SBNUC, DSR, Mosaicking and LACE for various imaging systems, showing signi£cant improvement of the image quality for several imaging conditions.

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.

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.

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.