We present a study of border surveillance systems for automatic threat estimation. The surveillance systems should allow border control operators to be triggered in time so that adequate responses are possible. Examples of threats are smuggling, possibly by using small vessels, cars or drones, and threats caused by unwanted persons (e.g. terrorists) crossing the border. These threats are revealed by indicators which are often not exact and evidence for these indicators incorporates significant amounts of uncertainty. This study is linked to the European Horizon 2020 project ALFA, which focuses on the detection and threat evaluation of low flying objects near the strait of Gibraltar. Several methods are discussed to fuse the indicators while taking the uncertainty into account, including Fuzzy Reasoning, Bayesian Reasoning, and Dempster-Shafer Theory. In particular the Dempster-Shafer Theory is elaborated since this approach incorporates evaluation of unknown information next to uncertainty. The method is based on belief functions representing the indicators. These functions show a gradual increase or decrease of the suspiciousness depending on input parameters such as object speed, size etc. The fusion methods give two output values for each track: a suspect probability and an uncertainty value. The complete dynamic risk assessment of detected flying objects is evaluated by the automatic system and targets with probabilities exceeding a certain threshold and appropriate uncertainty values are presented to the border control operators.
In current military operations threats should be monitored accurately. The use of sensors is indispensable for this
purpose, for example with camera and radar systems. Using data from such systems we have studied automated
procedures for extracting observable behavioral features of persons and groups, which can be associated with threats. We
have analysed algorithms for identifying animals versus humans, and for determining the activity of detected humans.
Secondly, geospatial algorithms are studied to determine people in suspicious places.
Radar satellites are important for geospatial intelligence about urban areas and urban situational awareness, since these
satellites can collect data at day and night and independently of weather conditions ensuring that the information can be
obtained at regular intervals and in time. For this purpose we have applied change detection techniques developed at
TNO to Radarsat I fine beam imagery of various dates to find changes in Baghdad during and after the war in 2003.
A drawback of SAR imagery is the poor ability to recognize the detected changes in the scene. In this paper we present a
workflow for the characterization and classification of changes detected in SAR imagery. We show that these changes
can be characterized using complementary data and context information. For this purpose we have used a digital surface
model from Ikonos stereo imagery that contains building heights. We also have used so-called temporal features
extracted from a multi-temporal data-set of Radarsat data to select the changes and to detect activity between 2003 and
2007, which has been classified with high-resolution optical data.
Maritime borders and coastal zones are susceptible to threats such as drug trafficking, piracy, undermining economical
activities. At TNO Defence, Security and Safety various studies aim at improving situational awareness in a coastal zone.
In this study we focus on multi-sensor surveillance of the coastal environment. We present a study on improving
classification results for small sea surface targets using an advanced sensor suite and a scenario in which a small boat is
approaching the coast.
A next generation sensor suite mounted on a tower has been defined consisting of a maritime surveillance and tracking
radar system, capable of producing range profiles and ISAR imagery of ships, an advanced infrared camera and a laser
range profiler. For this suite we have developed a multi-sensor classification procedure, which is used to evaluate the
capabilities for recognizing and identifying non-cooperative ships in coastal waters.
We have found that the different sensors give complementary information. Each sensor has its own specific distance
range in which it contributes most. A multi-sensor approach reduces the number of misclassifications and reliable
classification results are obtained earlier compared to a single sensor approach.
We have studied the robustness of features against aspect variability for the purpose of target discrimination using polarimetric 35 Ghz ISAR data. Images at a resolution of 10 cm and 30 cm have been used for a complete aspect range of 360 degrees. The data covered four military targets: T72, ZSU23/4, T62, and BMP2. For the study we composed several feature vectors out of individual features extracted from the images. The features are divided into three categories: radiometric, geometric and polarimetric. We found that individual features show a strong variability as a function of aspect angle and cannot be used to discriminate between the targets irrespectively of the aspect angle. Using feature vectors and a maximum likelihood classifier reasonable discrimination (about 80%) between the four targets irrespective of the aspect angle was obtained at 10 cm resolution. At 30 cm resolution less significant discrimination (less than 70%) was found irrespective of the kind of feature vector used. In addition we investigated target discrimination per 30-degree aspect interval. In order to determine the aspect angle of targets we used a technique based on the Radon transformation, which gave an accuracy of about 5 degrees in aspect angle. We found that in this case good discrimination (more than 90%) was obtained at 10 cm resolution and reasonable discrimination (about 80%) at 30 cm resolution. The results are compared with analogous results from MSTAR data (30 cm resolution) of comparable targets.
A study is presented in which several different representations of polarimetric SAR data for visual interpretation are evaluated. Using a group of observers the tasks 'land use classification' and 'object detection' were examined. For the study, polarimetric SAR data were used with a resolution of 3 meters. These data were obtained with the Dutch PHARUS sensor from two test areas in the Netherlands. The land use classes consisted of bare soil, water, grass, urban and forest. The objects were farmhouses. It was found that people are reasonably successful in performing land use classification using SAR data. Multi- polarized data are required, but these data need not to be fully polarimetric, since the best results were obtained with the hh- and hv-polarization combinations displayed in the red and green color channels. Detection of objects in SAR imagery by visual inspection is very difficult. Most representations gave minimal results. Only when the hh- and hv-polarization combinations were displayed in the red and green channels, somewhat better results were obtained. Comparison with an automatic classification procedure showed that land use classification by visual inspection appears to be the more effective. Automatic detection of objects gave better results than by visual inspection, but many 'false' objects were also detected.
Target detection and recognition using polarimetric SAR data has been studied by using PHARUS and RAMSES data collected during the MIMEX campaign. Additionally very high-resolution ISAR data was used. A basic detection and recognition scheme has been developed, which includes polarimetric speckle- filtering, CFAR detection and the extraction of geometrical, intensity and polarimetric features. From the SAR images we conclude that polarimetric features can be useful to discriminate targets from clutter. At resolutions of 1 meter or better, shape and orientation recognition can be obtained with these features. To classify the targets, other features or other techniques have to be used. Examples are polarimetric decomposition techniques, of which two have been explored using the ISAR data.