Coastguard and Navy assets are increasingly involved in Maritime Security Operations (MSO) for countering piracy,
weapons and drugs smuggling, terrorism and illegal trafficking. Persistent tracking of vessels in interrupted time series
over long distances and the modelling of intent and behaviour from multiple data sources are key enablers for Situation
Assessment in MSO. Results of situation assessment are presented for AIS/VTS observations in the Dutch North Sea and
for simulated scenarios in the Gulf of Oman.
More and more sensors are getting Internet connected. Examples are cameras on cell phones, CCTV cameras for traffic
control as well as dedicated security and defense sensor systems. Due to the steadily increasing data volume, human
exploitation of all this sensor data is impossible for effective mission execution. Smart access to all sensor data acts as
enabler for questions such as “Is there a person behind this building” or “Alert me when a vehicle approaches”.
The GOOSE concept has the ambition to provide the capability to search semantically for any relevant information
within “all” (including imaging) sensor streams in the entire Internet of sensors. This is similar to the capability provided
by presently available Internet search engines which enable the retrieval of information on “all” web pages on the
Internet. In line with current Internet search engines any indexing services shall be utilized cross-domain. The two main
challenge for GOOSE is the Semantic Gap and Scalability.
The GOOSE architecture consists of five elements: (1) an online extraction of primitives on each sensor stream; (2) an
indexing and search mechanism for these primitives; (3) a ontology based semantic matching module; (4) a top-down
hypothesis verification mechanism and (5) a controlling man-machine interface.
This paper reports on the initial GOOSE demonstrator, which consists of the MES multimedia analysis platform and the
CORTEX action recognition module. It also provides an outlook into future GOOSE development.
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.
At present, gas pipeline networks in Europe are routinely monitored
by vehicle and air patrols to protect them against damage by soil movement and third part interference. Because of the expenses, pipeline operators are investigating the possibilities to replace these traditional monitoring methods by remote sensing from space. A preliminary analysis shows that considerable savings can be achieved by deploying a user network of ground stations to receive the
Synthetic Aperture Radar (SAR) data of the ENVISAT, RADARSAT-2, ALOS and TerraSAR satellites.
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.