In the last years, there has been a huge improvement in Electro-Optical (EO) systems effectiveness, due to the availability of large staring arrays detectors with higher performance, as well as strong processing capability. So both in homeland surveillance and for military situational awareness, the use of EO systems, operating from Visible to Infrared, has dramatically grown.
Operations in Degraded Visual Environment (DVE) are frequent during military actions, due to many factors: either natural (poor light, fog, glare etc.) or intentionally produced (smoke, dust etc.). In these conditions the performance of EO sensors is degraded and therefore their effectiveness for Detection, Recognition and Identification (DRI) and Navigation capability. In general, the situational awareness is strongly affected as well as the safety of personnel. Proper techniques are needed to restore (at least partially) the imaging capabilities of EO sensors in DVEs. The project SPIDVE (Study on EO Sensors Performance Improvement in Degraded Visual Environment), promoted by the European Defense Agency (EDA), is focused on the analysis of the impact on EO sensors performance by the adverse visual conditions. It starts from the analysis of the status of the art in terms of technology, processing, measurements and modeling methodologies, based on the existing scientific literature, to carry out an assessment of the most promising technologies for image enhancement and restoration in different DVEs.
Particular care is devoted to the discussion with the final users (the military personnel) to identify the cases of higher interest for their operations. On this basis the possible candidate methodologies shall be analyzed more deeply, evaluating their performance with the aim of selecting the most promising one.
At the end, a possible roadmap for new initiatives to exploit and develop the findings shall be defined.
This paper addresses bathymetry estimation from high resolution multispectral satellite images by proposing an accurate supervised method, based on a neuro-fuzzy approach. The method is applied to two Quickbird images of the same area, acquired in different years and meteorological conditions, and is validated using truth data. Performance is studied in different realistic situations of in situ data availability. The method allows to achieve a mean standard deviation of 36.7 cm for estimated water depths in the range [−18, −1] m. When only data collected along a closed path are used as a training set, a mean STD of 45 cm is obtained. The effect of both meteorological conditions and training set size reduction on the overall performance is also investigated.
In this paper we study passive focusing techniques for infrared sensors. We present a survey of existing focus measures,
i.e. functionals that give an estimate of the quality of focus as a function of the lens position. We synthesize the material
proposed in the literature and show that all the approaches exploit the same general layout differing only for the choice
of the filtering technique used to extract the image details. We present and discuss experimental results obtained on real
infrared data taken in many operating conditions. The experimental analysis aims at comparing the quality of the focus
measures and at evaluating their impact of the subsequent algorithm that searches the best focus position of the lens. For
this purpose, we propose a comparative analysis based on three important properties of the focus measure: symmetry,
smoothness and peakdness.