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.
Optical imaging, including infrared imaging, generally has many important applications, both civilian and military. In recent years, technological advances have made multi- and hyperspectral imaging a viable technology in many demanding military application areas. The aim of the CEPA JP 8.10 program has been to evaluate the potential benefit of spectral imaging techniques in tactical military applications. This unclassified executive summary describes the activities in the program and outlines some of the results. More specific results are given in classified reports and presentations. The JP 8.10 program started in March 2002 and ended in February 2005. The participating nations were France, Germany, Italy, Netherlands, Norway, Sweden and United-Kingdom, each with a contribution of 2 man-years per year. Essential objectives of the program were to: 1) analyze the available spectral information in the optronic landscape from visible to infrared; 2) analyze the operational utility of multi- and hyperspectral imaging for detection, recognition and identification of targets, including low-signature targets; 3) identify applications where spectral imaging can provide a strong gain in performance; 4) propose technical recommendations of future spectral imaging systems and critical components. Finally, a stated objective of the JP 8.10 program is to "ensure the proper link with the image processing community". The presentation is organized as follows. In a first step, the two trials (Pirrene and Kvarn) are presented including a summary of the acquired optical properties of the different landscape materials and of the spectral images. Then, a phenomenology study is conducted analyzing the spectral behavior of the optical properties, understanding the signal at the sensor and, by processing spectroradiometric measurements evaluating the potential to discriminate spectral signatures. Cameo-Sim simulation software is presented including first validation results and the generation of spectral synthetic images. Results obtained on measured and synthetic images are shown and discussed with reference to two main classes of image processing tasks: anomaly detection and signature based target detection. Furthermore, preliminary works on band selection are also presented which aim to optimize the spectral configuration of an image sensor. Finally, the main conclusions of the WEAG program CEPA JP8.10 are give
In general, long range visual detection, recognition and identification are hampered by turbulence caused by atmospheric conditions. Much research has been devoted to the field of turbulence compensation. One of the main advantages of turbulence compensation is that it enables visual identification over larger distances. In many (military) scenarios this is of crucial importance. In this paper we give an overview of several software and hardware approaches to compensate for the visual artifacts caused by turbulence. These approaches are very diverse and range from the use of dedicated hardware, such as adaptive optics, to the use of software methods, such as deconvolution and lucky imaging. For each approach the pros and cons are given and it is indicated for which scenario this approach is useful. In more detail we describe the turbulence compensation methods TNO has developed in the last years and place them in the context of the different turbulence compensation approaches and TNO's turbulence compensation roadmap. Furthermore we look forward and indicate the upcoming challenges in the field of turbulence compensation.
Infrared imagery over long ranges is hampered by atmospheric turbulence effects, leading to spatial resolutions worse than expected by a diffraction limited sensor system. This diminishes the recognition range and it is therefore important to compensate visual degradation due to atmospheric turbulence. The amount of turbulence is spatially varying due to anisoplanatic conditions, while the isoplanatic angle varies with atmospheric conditions. But also the amount of turbulence varies significantly in time. In this paper a method is proposed that performs turbulence compensation using a patch-based approach. In each patch the turbulence is considered to be approximately spatially and temporally constant. Our method utilizes multi-frame super-resolution, which incorporates local registration, fusion and deconvolution of the data and also can increase the resolution. This makes our method especially suited to use under anisoplanatic conditions. In our paper we show that our method is capable of compensating the effects of mild to strong turbulence conditions.
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