This PDF file contains the front matter associated with SPIE Proceedings Volume 7114, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

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.

Quick and precise response is essential for riot squads when coping with escalating violence in crowds. Often it is just a single person, known as the leader of the gang, who instigates other people and thus is responsible of excesses. Putting this single person out of action in most cases leads to a de-escalating situation. Fostering de-escalations is one of the main tasks of crowd and riot control. To do so, extensive situation awareness is mandatory for the squads and can be promoted by technical means such as video surveillance using sensor networks. To develop software tools for situation awareness appropriate input data with well-known quality is needed. Furthermore, the developer must be able to measure algorithm performance and ongoing improvements. Last but not least, after algorithm development has finished and marketing aspects emerge, meeting of specifications must be proved. This paper describes a multisensor benchmark which exactly serves this purpose. We first define the underlying algorithm task. Then we explain details about data acquisition and sensor setup and finally we give some insight into quality measures of multisensor data. Currently, the multisensor benchmark described in this paper is applied to the development of basic algorithms for situational awareness, e.g. tracking of individuals in a crowd.

If for a given application, candidate tracking methods for humans need to be selected and optimized, then relevant sensor and truth data as well as appropriate assessment criteria are required. In the work reported in this contribution we used data recently collected in a riot control scenario. We then processed the sensor data using a set of tracking methods from literature. Tracking results and truth data allowed us to deduce metrics that reflect the usefulness of a tracking method for the selected scenario. The software implementation of the assessment criteria, together with sensor and truth data, forms a benchmark for tracking algorithms in a riot control scenario. It can be used by developers to optimize their tracking systems and to demonstrate their usefulness for application in a riot control scenario. The performance and robustness of optimized tracking methods can considerably improve situational awareness in a riot control scenario.

A wide variety of signal and image processing applications, including the US Federal Bureau of Investigation's fingerprint compression standard [3] and the JPEG-2000 image compression standard [26], utilize wavelets. This paper describes new research that demonstrates how a genetic algorithm (GA) may be used to evolve transforms that outperform wavelets for satellite image compression and reconstruction under conditions subject to quantization error. The new approach builds upon prior work by simultaneously evolving real-valued coefficients representing matched forward and inverse transform pairs at each of three levels of a multi-resolution analysis (MRA) transform. The training data for this investigation consists of actual satellite photographs of strategic urban areas. Test results show that a dramatic reduction in the error present in reconstructed satellite images may be achieved without sacrificing the compression capabilities of the forward transform. The transforms evolved during this research outperform previous start-of-the-art solutions, which optimized coefficients for the reconstruction transform only. These transforms also outperform wavelets, reducing error by more than 0.76 dB at a quantization level of 64. In addition, transforms trained using representative satellite images do not perform quite as well when subsequently tested against images from other classes (such as fingerprints or portraits). This result suggests that the GA developed for this research is automatically learning to exploit specific attributes common to the class of images represented in the training population.

The objective of this paper is to present the Swedish land mine and UXO detection project named "Multi Optical Mine Detection System", MOMS with emphasis on system concept development. The aim of the project is to define and evaluate some system concepts, one of which may results in further development. Research and investigations carried out within the MOMS project during the first 3.5 years (of 5 in total) will shortly be described. Activities have mainly been focused on basic principles, scenarios, phenomena, and experimental studies using different passive and active EO sensors including signal processing. Based on these results a number of system concepts are suggested involving both airborne and ground vehicles as well as different sensor combinations. The different concepts will be discussed in relation to overall performance such as coverage rate, ROC (receiver operating characteristics) and complexity. Areas of uncertainties will be identified and suggestions for further investigations will be proposed.

We describe progress in the second year of the EMRS DTC TEP theme project entitled "Temporal Resolution Enhancement from Motion". The aim is to develop algorithms that combine evidence over time from a sequence of images in order to improve spatial resolution and reduce unwanted artefacts. A C++ implementation of an algorithm was developed in year one 1. Work in year two has improved the efficiency and extended the applicability of the algorithm. New schemes for information update and scene matching have substantially reduced the processing time and enabled application of the technique to imagery with more complicated viewing geometries. The new technique is demonstrated using airborne infra-red imagery datasets from a Wescam MX series turret on a helicopter.

FOI (Sweden) and FGAN-FOM (Germany) have carried out common field trials using range gating imaging at 1.5 μm. Some examples showing the potential of bi-static vs. the common mono-static configurations are given. In addition the experiments include a comparison between active SWIR and passive EO-IR imaging. The paper discusses the results with respect to suitability for defence and security applications.

We report on a wideband lidar-radar architecture in which range resolution is independent of pulse duration thanks to the use of a widely tunable intensity-modulated laser combined with a radar-like signal processing. As a source, we use a dual-frequency laser which provides a 100 % depth intensity modulated beam at GHz frequencies. The frequency is electrically adjustable over 1 GHz and is stabilized with a fiber-loop based frequency discriminator. With this setup, we generated a 1 GHz bandwidth stepped-frequency waveform. A range resolution of 22 cm is demonstrated.

EADS Germany is the world market leader in commercial and military Helicopter Laser Radar (HELLAS) Obstacle Warning Systems. The HELLAS-Warning System has been introduced into the market in 2000, is in service at German Federal Police and Royal Thai Air Force. HELLAS was also successfully evaluated by the Foreign Comparative Test Program (FCT) of the U.S. Army and other governmental agencies. Currently the successor system for military applications, HELLAS-Awareness, is in qualification phase. It will have extended sensor performance, enhanced real-time data processing capabilities and advanced human machine interface (HMI) features. Flight tests on NH90 helicopter have been successfully performed. Helicopter series integration is scheduled to begin from 2009. We will give an outline of the new sensor unit concerning detection technology and helicopter integration aspects. The system provides a widespread field of view with additional dynamic line of sight steering and a large detection range in combination with a high frame rate. We will show the HMI representations. This HELLAS system is the basis for a 3 dimensional see-and-remember-system for brown-out recovery. When landing in sandy or dusty areas the downwash of the helicopter rotor causes clouds of visually-restrictive material that can completely obstruct the pilot's outside reference, resulting in a complete loss of situational awareness and spatial orientation of the pilot which can end up in total loss of aircraft control and dangerous accidents. The brown-out recovery system presented here creates an augmented enhanced synthetic vision of the landing area with the surrounding which is based on HELLAS range image data as well as altimeter and inertial reference information.

Results from an experimental polarimetric investigation of 7 different types of land mines and 3 types of plants with the aim to explore the possibility in discriminating surface land mines from natural backgrounds are presented. The samples Mueller matrices at both specular and non-specular angles during 405 nm 1570 nm laser illumination were collected. Also included in this study is reflection spectra from the mines taken from 400 to 2500 nm as well as actual images of surface land mines hidden in a natural environment during different weather conditions. The mines had a reflection coefficient between 5-15 % with peak values around 510 nm due to the embedded green pigment. The mines were found to be less reflective in wet compared to dry conditions. The polarimetric study revealed that the samples had similar retardance and diattenuation values for small incident angles, but that discrimination between the samples could be made by monitoring the depolarization of the incident light for several incident angles, as a function of the angular distribution of scattered light. The land mines generally experience less depolarization than the investigated plants, specifically for specular angles around 1570 nm where the mines act as a non-depolarizing sample with depolarization index close to 1.0. The depolarization index is significantly smaller for specular angles from the plants, becoming 0.4 or below. Both plants and mines experience more depolarization for non-specular angles. A non-specular angular scan with a constant bi-static angle resulted in a Lorentzian shaped depolarization index curve, with characteristic differences in the fitted line-shape parameters. Remote laser based polarimetry might thus be a promising supplementary technique in recognizing surface mines or other related man-made objects from a natural background. Conclusively, the depolarization index as a function of angular distribution of scattered light along with its wavelength dependence is a metric that produce significant differences in the polarimetric signatures.

We present an overview of the current status of laser diodes used in remote sensing application including novel laser types such as single mode emitting DFB lasers operating at wavelengths up to 3 μm and quantum cascade lasers for mid infrared absorption spectroscopy. In particular we will focus on applications of these devices in the frame of safeguard measures and home security.

The EO probe developed, offers an accurate evaluation of only one component of either continuous or single shot electric signal as long as the electric field to be measured is strong enough. Since those probes are also non intrusive, very small (tens of microns width) and have a flat response over a very large bandwidth (more than seven decades), they are competitive candidates for accurate vectorial measurement of either radiated or guided high power microwave electric field in the far- and near-field region. Unfortunately what makes them so versatile is also their Achilles' heel: the strong temporal instability of their response. Therefore, we present, in this paper, a fully-automated electro-optic probe developed to stabilise the transducer.

For certain applications that involve faithfully transmitting microwave signals around or between military platforms, it is essential to use RF-on-fibre or an RF-photonic link. This is especially true for a broadband RF signal which, when used to modulate a 200 THz optical carrier wave, becomes effectively very narrow band and so does not suffer the effects of dispersion or frequency dependent loss. The main reason why RF-photonics is not more widely used is because it is difficult to achieve the often very severe dynamic range requirements of many military systems. In this paper we describe our approach to making highly linearised electrical-to-optical transducers (electro-optic waveguide modulators) using a slightly modified version of a high-throughput gallium arsenide p-HEMT fabrication process. To make high dynamic range linearised modulators requires the unprecedented precision in fabrication and manufacture that this approach allows. We describe our most recent results on tuneable linearised modulators.

Detection of point targets becomes increasingly more difficult as targets become weak and engagement takes place in highly dense, varying and complex background like clouds. To detect weak point targets in this scenario, detection threshold should be sufficiently low. And this leads to high false alarm rate. In order to make detection system robust to dense clutter (we mean 'clouds') and noise, post processing algorithms are required. Almost all detection/tracking systems use post processing techniques, but less has been reported in this area. In this paper, we propose a simple and computationally efficient post processing algorithm to encounter false alarms due to dense and varying clouds. Models for target and cloud edges are presented. Results demonstrate that proposed algorithm is able to reduce false alarms to a large extent.

The UK is globally recognised as strong in Photonics. However its Photonics sector is fragmented and the size and sectors of interest have not previously been established. The UK government has instigated the formation of the Photonics Knowledge Transfer Network (PKTN) to bring the Photonics community together. The UK features in Defence & Security; Communications; Measurement; Medical Technology; Lighting; Solar Energy; Information Technology and Flat Panels. This expertise is scattered through out the UK in geographic areas each with a breadth of Photonic interests. The PKTN has mapped the UK capability in all Photonics sectors. This paper will present the capability of the Companies, Research Institutions and Infrastructure making up the Defence & Security Photonics scene in the UK. Large Defence companies in the UK are well known throughout the world. However, there are a large number of SMEs, which may not be as well known in the supply chain. These are being actively encouraged by the UK MoD to engage with the Defence & Security Market and shall be discussed here. The presentation will reference a number of organisations which help to fund and network the community, such as the Defence Technology Centres. In addition the Roadmap for Defence & Security in the UK, produced for the UK Photonics Strategy (July 2006) by the Scottish Optoelectronics Association will be described and the plans in taking it forward under the PKTN will be revealed.

We present recent advances in high power semiconductor laser bars and arrays at near infrared and eye-safe wavelengths. We report on increased spectral brightness with internal gratings to narrow and stabilize the spectrum and increased spatial brightness in multimode and single mode devices. These devices have the potential to dramatically improve diode pumped systems and enable new direct diode applications.

This study employs the developed algorithm for retrieving land surface temperature (LST) from Landsat TM over Saudi Arabia. The algorithm is a mono window algorithm because the Landsat TM has only one thermal band between wavelengths of 10.44-12.42 μm. The proposed algorithm included three parameters, brightness temperature, surface emissivity and incoming solar radiation in the algorithm regression analysis. The LST estimated by the proposed developed algorithm and the LST values produced using ATCORT2_T in the PCI Geomatica 9.1 image processing software were compared. The mono window algorithm produced high accuracy LST values using Landsat TM data.

The detection of objects from a cluttered background using remote sensing data may cause many false alarms if the target object and the background have overlapping spectra. In this study, we propose an integrated approach to combine pixel-based spectral labeling with object-based spatial property measures. A hierarchical structure is developed in which multileveled attributions and decision rules can be implemented. The targets are then extracted progressively. Experimental results show a substantial reduction in the number of false alarms with the proposed method.

Nowadays, air quality is a major concern in many countries whether in the developed or the developing countries. Due to the high cost and limited number of air pollutant stations in each area, they cannot provide a good spatial distribution of the air pollutant readings over a city. Satellite observations can give a high spatial distribution of air pollution. The objective of this study is to test the feasibility of using Landsat TM for retrieving the concentration of the particulate matter of size less than 10- micron (PM10). The retrieval of surface reflectance is important to obtain the atmospheric reflectance in remotely sensed data and later used for algorithm calibration. In this study, we retrieve the surface reflectance using the relationship between the two visible bands (blue and red) and the mid infrared data at 2.1 μm. We use the assumption that the mid infrared band data is not significantly affected by atmospheric haze. An algorithm was developed based on the aerosol properties to correlate the atmospheric reflectance and PM10. We also evaluate the used of the thermal band in the air quality study which is added into the proposed regression algorithm. The in situ PM10 data were collected simultaneously with the acquired satellite image. A high correlation coefficient (R) was obtained in this study between the measured and predicted PM10 values. Finally, a PM10 map was generated using the proposed algorithm and geometrically corrected. The generated PM10 was also colour coded for visual interpretation and smoothed using an average filter to minimize the random noise. This study indicated that the Landsat TM can be a very good tool for air quality study.

Remote sensing using the satellite borne LIDAR systems are currently providing new features for global atmospheric sensing from space. The LIDAR on board the Cloud Aerosol LIDAR and Infrared Pathfinder Satellite Observation (CALIPSO) satellite is currently obtaining global aerosol and cloud measurements from space since launched on April 28, 2006. The CALIPSO satellite carries a polarization-sensitive LIDAR system that records backscatter measurements at 532 nm and 1064 nm. In this study, we investigated the stratospheric aerosol backscatter coefficients over Peninsular Malaysia. An initial result of actual data supports that the CALIPSO LIDAR data exhibits sensitivity to the presence of stratospheric aerosol in this study area.

The sea surface temperature (SST) mapping could be performed with a wide spatial and temporal extent in a reasonable time limit. The space-borne sensor of AVHRR was widely used for the purpose. However, the current SST retrieval techniques for infrared channels were limited only for the cloud-free area, because the electromagnetic waves in the infrared wavelengths could not penetrate the cloud. Therefore, the SST availability was low for the single image. To overcome this problem, we studied to produce the composite of three day's SST map. The diurnal changes of SST data are quite stable through a short period of time if no abrupt natural disaster occurrence. Therefore, the SST data of three consecutive days with nearly coincident daily time were merged in order to create a three day's composite SST data. The composite image could increase the SST availability. In this study, we acquired the level 1b AVHRR (Advanced Very High Resolution Radiometer) images from Malaysia Center of Remote Sensing (MACRES). The images were first preprocessed and the cloud and land areas were masked. We made some modifications on the technique of obtaining the threshold value for cloud masking. The SST was estimated by using the day split MCSST algorithm. The cloud free water pixels availability were computed and compared. The mean of SST for three day's composite data were calculated and a SST map was generated. The cloud free water pixels availability were computed and compared. The SST data availability was increased by merging the SST data.