Prediction about how migrants move in the preborderterrain is important for effective border control. In thispaper we present a method to obtain permeability indicators foraccessibility and concealment on the basis of geographical terrainfeatures, derived from high resolution satellite data. Theindicators are used to estimate the mobility of migrants. A modelto predict the density of migrants arriving at the border, and toassess the impact of security measures, is introduced anddiscussed. The model was implemented and tested. The resultsare used as value adding products in pre-operational services forborder control. Validation has to be done using actual irregularborder-crossing geo-oriented statistics. By the time this paperwas written no such statistics were available.

Maritime situation awareness is supported by a combination of satellite, airborne, and terrestrial sensor systems. This paper presents several solutions to process that sensor data into information that supports operator decisions. Examples are vessel detection algorithms based on multispectral image techniques in combination with background subtraction, feature extraction techniques that estimate the vessel length to support vessel classification, and data fusion techniques to combine image based information, detections from coastal radar, and reports from cooperative systems such as (satellite) AIS. Other processing solutions include persistent tracking techniques that go beyond kinematic tracking, and include environmental information from navigation charts, and if available, ELINT reports. And finally rule-based and statistical solutions for the behavioural analysis of anomalous vessels. With that, trends and future work will be presented.

This paper aims at the segmentation of seafaring vessels in optical satellite images, which allows an accurate length estimation. In maritime situation awareness, vessel length is an important parameter to classify a vessel. The proposed segmentation system consists of robust foreground-background separation, wake detection and ship-wake separation, simultaneous position and profile clustering and a special module for small vessel segmentation. We compared our system with a baseline implementation on 53 vessels that were observed with GeoEye-1. The results show that the relative L1 error in the length estimation is reduced from 3.9 to 0.5, which is an improvement of 87%. We learned that the wake removal is an important element for the accurate segmentation and length estimation of ships.

Potential asymmetric threats at short range in complex environments need to be identified quickly during coastal operations. Laser range profiling is a technology that has the potential to shorten the OODA loop (Orient, Observe, Detect, Act) by performing automatic characterisation of targets at large distance. The advantages of non-cooperative target recognition with range profiles are: (a) a relatively short time on target is required, (b) the detection range is longer than in the case of passive observation technologies such as IRST, and (c) characterisation of range profiles is possible at any aspect angle. However, the shape of a range profile depends strongly on aspect angle. This means that a large data set is necessary of all expected targets with reference profiles on a very dense aspect angle grid. Analysis of laser range profiles can be done by comparing the measured profile with a database of laser range profiles obtained from 3D models of possible targets. An alternative is the use of a profile database from one or several measurement campaigns. A prerequisite for this is the availability of enough measured profiles of the appropriate targets, for many aspect angles. Comparison of measured laser range profiles with a reference database can be performed using, e. g., formal statistical correlation techniques or histogram dissimilarity techniques.In this work, a field trial has been conducted to validate the concept of identification by using a laser range profiling system with a high bandwidth receiver and short laser pulses. The field trial aimed at characterization of sea-surface targets in a coastal/harbour environment. The targets ranged from pleasure boats like sailing boats, jet skis, and speed boats to professional vessels like barges, cabin boats, and military vessels, all ranging from 3 to 30 meters in length. We focus on (a) the use of a reference database generated via 3D target models, and (b) the use of a reference database of measured laser range profiles. A variety of histogram dissimilarity measures was examined in order to enable fast and reliable classification algorithms.

A priori information on suspicious behaviour is extremely valuable for countering threats involving improvised explosive devices (IEDs). Suspicious activities along routes during expeditionary operations can be monitored by unattended networks using simple sensing nodes that can gather data for continuous monitoring of daily vehicle activity. Dedicated software yields the necessary intelligence on these activities by filtering suspicious behaviour from anomalous behaviour (including false alarms). Research has started to equip a commercially available sensor network with data analysis software. It aims at demonstrating the detection of suspicious behaviour along roads, within a required time span. Three phases are distinguished. First phase is the analysis of traffic flux in a simple scenario with three networks lying at three junctions. The second phase investigates the ability to track and classify one object in this scenario, while the third phase aims to track and classify two or more objects. Findings are presented for phase one, flux measurements.A priori information on suspicious behaviour is extremely valuable for countering threats involving improvised explosive devices (IEDs). Suspicious activities along routes during expeditionary operations can be monitored by unattended networks using simple sensing nodes that can gather data for continuous monitoring of daily vehicle activity. Dedicated software yields the necessary intelligence on these activities by filtering suspicious behaviour from anomalous behaviour (including false alarms). Research has started to equip a commercially available sensor network with data analysis software. It aims at demonstrating the detection of suspicious behaviour along roads, within a required time span. Three phases are distinguished. First phase is the analysis of traffic flux in a simple scenario with three networks lying at three junctions. The second phase investigates the ability to track and classify one object in this scenario, while the third phase aims to track and classify two or more objects. Findings are presented for phase one, flux measurements.

Intelligence on abnormal and suspicious behaviour along roads in operational domains is extremely valuable for countering the IED (Improvised Explosive Device) threat. Local sensor networks at strategic spots can gather data for continuous monitoring of daily vehicle activity. Unattended intelligent ground sensor networks use simple sensing nodes, e.g. seismic, magnetic, radar, or acoustic, or combinations of these in one housing. The nodes deliver rudimentary data at any time to be processed with software that filters out the required information. At TNO (Netherlands Organisation for Applied Scientific Research) research has started on how to equip a sensor network with data analysis software to determine whether behaviour is suspicious or not. Furthermore, the nodes should be expendable, if necessary, and be small in size such that they are hard to detect by adversaries. The network should be self-configuring and self-sustaining and should be reliable, efficient, and effective during operational tasks - especially route surveillance - as well as robust in time and space. If data from these networks are combined with data from other remote sensing devices (e.g. UAVs (Unmanned Aerial Vehicles)/aerostats), an even more accurate assessment of the tactical situation is possible. This paper shall focus on the concepts of operation towards a working intelligent route surveillance (IRS) research demonstrator network for monitoring suspicious behaviour in IED sensitive domains.

Sea basing operations in coastal environments require a rapid and accurate description of the physical conditions in the region. Battlespace characterization and sensor performance assist in optimizing the efficiency and safety of operations, of which the detection of targets at low level above the sea surface is all-important. The environmental conditions of the marine boundary layer (MBL) - due to weather and atmospheric effects - change continuously in space and time, which certainly holds for the aerosol make-up. Models have been developed to describe the electro-optical propagation in the boundary layer as a function of meteorological parameters. EOSTAR is such an end-to-end model suite for EO sensor performance in which the Advanced Navy Aerosol Model (ANAM) is embedded for computing the aerosol extinction. While ANAM provides favourable results in open ocean conditions, in coastal zones the model lacks accuracy due to the presence of aerosols from a variety of sources that need to be assessed. In offshore wind conditions continental aerosols of anthropogenic and natural origin mix with marine aerosols produced in the surf zone and by wave breaking further offshore. Radiometers on satellites can be used to retrieve the spatial variation over an extended area determined by the swath width, with a resolution determined by the radiometer pixel size. In this contribution we explore the potential of satellite measurements to provide information on the aerosol properties over the range of interest in order to correctly handle their influence on transmission characteristics in the coastal zone. Results from measurements of the multidisciplinary Maritime REA/Battlespace Preparation 2007 trial, held during 20 April and 5 May 2007 near the vicinity of the island Elba along the west coast of Italy, are presented in this analysis. For one particular day, the satellite retrieved aerosol optical thickness (AOT) is to be compared with hand-held sun photometer measurements for quality assessment. The AOT values are converted into aerosol extinction coefficients for a pre-defined path. For one visible wavelength channel the transmission loss is computed with these coefficients and is compared with the computed transmission loss for the path in case of a) a single extinction coefficient obtained from measurements and b) a modeled extinction coefficient obtained from ANAM.

The dual view of the Advanced Along Track Scanning Radiometer (AATSR) allows for the accurate retrieval of aerosol properties over land using an algorithm in which these two views are used to eliminate the influence of the land reflectance on the top of the atmosphere radiation. The algorithm uses the AATSR IR and visible wavebands for cloud detection and the visible wavebands for aerosol retrieval. The retrieval is based on minimizing the error function between modeled and measured TOA reflectances, using all available wavelengths. The TOA reflectances are modeled for a variety of aerosol mixtures. Hence, both the aerosol optical depth at various wavelengths (and thus the Angstrom coefficient) and the mixing ratio of the dominant aerosol types can be determined. The results are evaluated by comparison with independent data: sun photometers and, when available, aerosol composition. In this contribution, the AATSR results are evaluated, based on comparisons between AATSR and MODIS AOD, and AERONET data. Results are used for assimilation in a regional scale chemistry transport model. The goal is to use satellite data for the determination of PM2.5. Other applications at TNO are in the synergisitic use with other satellites such as MSG SEVIRI and OMI.

The detection of targets at low levels above the sea surface by electro-optical (EO) sensors is affected by the atmosphere. Models have been developed to describe the electro-optical propagation in the marine atmospheric surface layer as a function of meteorological parameters. EOSTAR is an end-to-end model suite for EO sensor performance in which the Advanced Navy Aerosol Model (ANAM) is embedded for computing the aerosol extinction. While ANAM provides favorable results in open ocean conditions where the aerosols predominantly consist of sea salt particles, the model lacks accuracy in coastal zones due to the presence of aerosols from a variety of other sources. In offshore wind conditions continental aerosols of anthropogenic and natural origin mix with marine aerosols produced in the surf zone and by wave breaking further offshore. In principle, ANAM can be extended with the various aerosol types that may occur in the coastal zone, but to correctly handle their effect on EO propagation, information is required on the actual aerosol mixture over the range of interest. In this contribution we explore the potential of satellite instruments to provide this information. Radiometers on satellites can be used to retrieve the spatial variation over an extended area determined by the swath width, with a resolution determined by the radiometer pixel size. Input into this retrieval is a model describing the aerosol mixture in varying ratio, e.g. a mixture of continental and marine aerosol. While the marine component can be constrained by ANAM using local meteorological input parameters, the continental component can be retrieved and used as input to determine the fine particle distribution in ANAM.

Aerosol properties are retrieved from ATSR-2 data (ERS- 2 satellite) by utilizing the dual view for application over land and the single view for application over water. These two algorithms have been merged into a fast and efficient algorithm that allows for near real-time processing and which is suitable for operational use. It includes correc- tions for surface and atmospheric effects including fully automated cloud screening procedures. The algorithm can be applied to retrieve aerosol optical depth (AOD), Ång- ström parameter and aerosol types. The application to data for the year 2000 over Europe is presented. The re- trieved AOD over land compares favorably with collo- cated sun-photometer data from the Aerosol Robotic Network (AERONET).