This paper presents the Swedish land mine and UXO detection project "Multi Optical Mine Detection System," MOMS, and the research carried out so far. The goal for MOMS is to provide knowledge and competence for fast detection of mines, especially surface laid mines, by the use of both active and passive optical sensors. A main activity was to collect information and gain knowledge about phenomenology; i.e. features or characteristics that can give a detectable signature or contrast between object and background, and to carry out a phenomenology assessment. A large effort has also been put into a scene description to support phenomenology assessment and provide a framework for further experimental campaigns. Also, some preliminary experimental results are presented and discussed.
The objective of this paper is to present the Swedish land mine and UXO detection project "Multi Optical Mine Detection System", MOMS. The goal for MOMS is to provide knowledge and competence for fast detection of mines, especially surface laid mines. The first phase, with duration 2005-2009, is essentially a feasibility study which focuses on the possibilities and limitations of a multi-sensor system with both active and passive EO-sensors. Sensor concepts used, in different combinations or single, includes 3-D imaging, retro reflection detection, multi-spectral imaging, thermal imaging, polarization and fluorescence. The aim of the MOMS project is presented and research and investigations carried out during the first years will be described.
As a part of the Swedish mine detection project MOMS, an initial field trial was conducted at the Swedish EOD and
Demining Centre (SWEDEC). The purpose was to collect data on surface-laid mines, UXO, submunitions, IED's, and
background with a variety of optical sensors, for further use in the project. Three terrain types were covered: forest,
gravel road, and an area which had recovered after total removal of all vegetation some years before. The sensors used in
the field trial included UV, VIS, and NIR sensors as well as thermal, multi-spectral, and hyper-spectral sensors, 3-D laser
radar and polarization sensors. Some of the sensors were mounted on an aerial work platform, while others were placed
on tripods on the ground. This paper describes the field trial and the presents some initial results obtained from the
subsequent analysis.
The objective of this paper is to present the Swedish land mine and UXO detection project named "Multi Optical Mine Detection System," MOMS. Research and investigations carried out within the MOMS project during the first year will be described. Activities have mainly been focused on basic principles, phenomena, acquisition of knowledge and literature studies. The paper introduces the reader with the aim of the project and then the initial and future work is
presented.
The Swedish Defence Research Agency (FOI) has presented several approaches to temporal analysis of thermal IR data in the application of mine detection during the years. Detection by classification is performed using a number of detection algorithms with varying, in general good, results. The FOI temporal analysis method is tested on images randomly chosen from a diurnal sequence. The test sequence show very little contrast. The reference features are taken from a known object in the scene or from a numerical model of the object of interest. In this paper variations of the method are evaluated on the same test data. Focus is on the question if increased number of data collection times affects the detection rate and false alarm rate. The ROC curves show performance better than random for all of the tested cases, and excellent for some. Detection rate increases and false alarm rate decreases with increased number of images used for some of the tested cases.
The overall objective of this paper is to improve the understanding of thermodynamic mechanisms around buried objects. The purpose is to utilize most favourable conditions for detection and also to enhance and evaluate other detection methods shown in a companion paper. This paper focuses on physical based models and simulations with measured data as boundaries for different situations of buried objects. For numerical models some assumptions of the real environment and boundaries have to be made, this paper shows the effects of different approaches of these assumptions. The investigations are carried out using a FEM approach with measured weather data as well as different sub models for the boundaries. All modelling works are carried out very in close connections with experiments with the purpose to achieve high accordance between measured and simulated values. This paper shows experimental and simulated results and discusses also the temporal analysis of thermal IR data.
Images recorded in ground areas potentially containing surface laid land mines are considered. The first hypothesis is that the image is of clutter (grass) only, while the alternative is that the image contains a partially occluded (covered) land mine in addition to the clutter. In such a scenario, the occlusion pattern is unknown and has to be treated as a nuisance parameter. In a previous paper it was shown that deterministic treatment of the unknown occlusion pattern, in companion with the applied model, renders a substantial increase in detector performance as compared to employment of the traditional additive model. However, a deterministic assumption ignores possible correlation and additional gains could be possible by taking the spatial properties into account. In order to incorporate knowledge regarding the occlusion, the spatial distribution is characterized in terms of an underlying Markov Random Field (MRF) model. A major concern with MRF models is their complexity. Therefore, in addition to this, a less computationally demanding technique to accommodate the occlusion behavior is also proposed. The main purpose of this paper is to investigate if significant gains are possible by acknowledging the spatial dependence. Evaluation on data using real occluded targets however indicates that the gain seem to be marginal.
The overall objective of this work is to investigate the possibilities of using airborne IR sensors for the purpose of detecting minefield features, such as land mines. A method is proposed for temporal analysis by extracting relevant information from diurnal IR images utilizing a combination of thermodynamic modelling, signal and image processing. This paper presents results from a field test of level 2 survey in May 2003 of suspected mine-polluted areas in Croatia. Airborne data was acquired using an IR sensor mounted on a rotary wing UAV. A weather station was used to collect weather data, and pt-100 temperature sensors recorded the temperature gradient in the soil and in reference markers that were used for calibrating the IR camera. The proposed method compares simulated temporal temperature with image data collected at several times during a diurnal cycle from the same area, pixel by pixel. The images are co-registered and calibrated with respect to reference values. The numerical model is based on physical laws and is set with relevant properties, geometries, materials, surface coefficients and the influence of the actual weather sets the boundary conditions. This paper shows some results from using temporal features for detection of different relevant objects in a real minefield.
KEYWORDS: Land mines, Mining, Data modeling, Sensors, Finite element methods, Optical testing, Infrared cameras, Shortwaves, Temperature metrology, Aluminum
This paper presents preliminary analysis of the data from measurements on a minefield in Croatia done in the international cooperation project Airborne Minefield Area Reduction (ARC). Temperature differences above and around suspected mines and minefield indicators, were recorded with a long wave IR camera in 8-9 micrometers , over a time of several days, capturing data under different weather conditions. The data are compared to simulations of land mines, minefield indicators and other objects using a themodynamic FEM model, developed at FOI. Different detection methods are presented and applied to the data.
This paper presents activities concerning optical detection of landmines at FOI, former FOA. The work is focused on the understanding of the origin of detectable optical signatures for choosing the most favorable conditions for detection. Measurements in test beds and calculations using a thermodynamic FEM model with conditions similar to those of the measurements are compared and interpreted in order to explain the behavior of the contrast. Examples will be given on modeling of buried landmines in soil. The heat flow as well as moisture flow has been taken into consideration. The diurnal heat exchange between the soil surface and the atmosphere generates the contrasts in the infrared images. Calculated temperature differences between the background and the surface above the buried object are compared to measured data from experiments. Results are presented and show how the temperature differences can vary over a 24-hour period. The variation depends on the weather at the time as well as the weather before the measurements started. Results from processing and analysis of temporal variations of optical signals from buried landmines and backgrounds are presented as well as their relation to weather parameters. A detection approach including the Likelihood Ratio Test (LRT) is presented. Some of the work has been carried out in an international cooperation project, Airborne Minefield Area Reduction (ARC). The objective is to develop, demonstrate and promote a new system for performing the UN Level 2 surveys allowing a quick reduction of suspected mine polluted areas and post cleaning quality control.
This paper presents the work on the detection of land mines using IR-images. Experiments have been performed where outdoor time series of IR contrast have been measured for wax filled antitank mines in sand and for real mines in a gravel road. For antitank mines in the sand box the contrast dependence of time lap between burial and measurement has been analyzed for a period of four months. The diurnal contrast variation of an anti tank mine buried for two and a half year in a gravel road has been calculated. Statistical correlation between apparent temperatures and weather parameters for different cases have been calculated. The purpose is to understand the origin of the contrast and to be able to predict the contrast at different times and under different conditions.
As the performance of systems for surveillance, reconnaissance, target detection, target recognition and target identification increases in competition with the increased skill in reduction of IR-signatures, there has been an increasing demand for analyzing and predicting the spatial properties of targets and backgrounds. The temporal variations of spatial properties, measured as texture, for object and background is of vital importance for target detection and assessment of signature reduction methods. One important question to be answered is: how does the texture for objects and backgrounds vary as a function of environment parameters e.g. weather? If that question could be answered, one important part of the problem of performing signature forecast could be solved. In an attempt to predict the dependences between spatiotemporal IR-signatures and weather parameters, the diurnal time series of different texture measures for different areas in a natural background scene have been measured and related to different weather parameters e.g. incidence, temperature and humidity. Examples of covariations between texture measures and weather parameters will be given in the paper.
Many of different descriptors of spatial properties of natural terrain and objects, in particular different texture descriptors, have been implemented. Using results from detection theory and image quality studies a set of texture measures has been selected by investigation of the amount of necessary uncorrelated measures. Using these we are able to measure the statistical multidimensional difference between terrain areas and object areas in a way that correlates with target acquisition performance.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.