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.
Computer programs for prediction of optical signatures of targets and backgrounds are valuable tools for signature assessment and signature management. Simulations make it possible to study optical signatures from targets and backgrounds under conditions where measured signatures are missing or incomplete. Several applications may be identified: Increase understanding, Design and assessment of low signature concepts, Assessment of tactics, Design and assessment of sensor systems, Duel simulations of EW, and Signature awareness. FOI (the Swedish Defence Research Agency) study several methods and modeling programs for detailed physically based prediction of the optical signature of targets in backgrounds. The most important commercial optical signature prediction programs available at FOI are CAMEO-SIM, RadThermIR, and McCavity. The main tasks of the work have been: Assembly of a database of input data, Gain experience of different computer programs, In-house development of complementary algorithms and programs, and Validation and assessment of the simulation results. This paper summarizes the activities and the results obtained. Some application examples will be given as well as results from validations. The test object chosen is the MTLB which is a tracked armored vehicle. It has been used previously at FOI for research purposes and therefore measurement data is available.
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
The hypothesis of this study was to find out whether it is possible to capture Woelfflin's basic concepts using methods within CBIR for estimating global characteristics of art works. If results from regression analysis of behavioral data can be linked to global spectral and spatial characteristics of the same art works, then this would substantiate our hypothesis. From a regression analysis assuming a linear relationship between trained observers' ratings of art works representing Woelfflin's concepts and three global image processing features commonly used in CBIR and assumed to be significantly related to the same concepts, we found results that give support to our hypothesis - it seems possible to grasp some of the art concepts by CBIR methods.
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.
When using prediction programs for optical signatures, it is necessary to include validations to find estimates of the uncertainties and define the regions of validity. In this paper we present two paths of development of validation methods: The objective of the first path is to analyze and validate the differences between simulated and measured images, through image features such as edge concentration and different energy measures. In particular, aspects that are important for detection, classification and identification of targets are considered. The second path concerns development of methods for quantifying the propagation of input data uncertainties to output parameters in computational predictions. Two commercial codes have been used for the modeling: RadThermIR for thermal predictions of the targets and CAMEO-SIM for the radiometry and rendering. A recently developed interface between the two codes has been utilized. For the validation of spatial statistics, several feature values have been computed for a measured image and for the corresponding simulated image. It was found that the agreement was quite good. The work on propagation of uncertainties in computational predictions has resulted in a number of proposed methods. In this paper we present two different methods: one based on linear error propagation and one based on the Monte Carlo method. The results are according to expectations for both types of methods and show that a large part of the uncertainty in predicted temperature emanates from input parameter uncertainties for the considered test case.
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.
Developments in the area of signature suppression make it progressively more difficult to recognize targets. Due to the high resolution of modern sensors it is necessary to focus on a wide range of target and partial target sizes, i.e. small structures as well as the whole target. Measures of the difference between targets and background are crucial when assessing signature reduction efforts. These measures should to some extent be associated with the process of detection of targets in background. Two approaches are feasible, trying to simulate human performance or using an autonomous sensor. In both cases we have to rely on a set of features discriminating targets from the background. In the spatial domain we need filters on different scales. The smallest filter will not be able to catch statistical features but has to be based on the use of small image parts like blobs and lines. Larger filter will give statistically relevant feature values. In addition, spectral properties can be used in a multi-dimensional approach investigating targets on different scales, i.e. from very low-resolution to well-resolved objects. Experiments with a new set of features and the use of linear discriminant analysis to get overall signature assessment values are described.
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.
Developments in the area of signature suppression make it progressively more difficult to recognize targets. The emphasis has been on the reduction of distinct features, like hot spots in the infrared band. Thus, in order to obtain a low false alarm rate, threat sensors have to utilize more information, i.e. spatial and spectral properties. The purpose of our work is to develop a general tool for camouflage assessment. The approach proposed in this paper is to apply texture descriptors to quantify the similarity between different parts of an image. In addition, other descriptors are used to distinguish man-made object characteristics. The selection of an appropriate set of features is discussed. The assumption is that an area containing observable targets has different statistics than other areas. Statistical properties along with detected target specific features have to be combined with methods used in data fusion. An experiment with a data set of 44 reference images has been carried out, using a recently developed computer program called Terrtex. High correlation with perception experiments was achieved using only one or two texture features. The importance of a careful selection of background area size is finally discussed.
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.
Shape and shape disruption have significant influence to the human target acquisition mechanism. A special testing method (the so called `photo-simulation') was developed in the eighties to present a set of image slides of camouflaged and not camouflaged objects in preferably natural backgrounds to military personnel to quantify differences in object camouflage effectiveness. Statistically significant results were achieved, however, the high test requirements limited its practical use. The project is motivated by an urgent need for a camouflage evaluation system based on computer vision with a fast response so that the user in a field test can be supported to further improve his camouflage skills. Hence, the photo simulation method cannot be regarded as obsolete, it can be used to compare the results of the camouflage evaluation system with the results of human perception. With an human-in-the-loop computer based camouflage assessment system, processing should be sped up by some orders of magnitude, could be automated for field tests and would yield several additional features. To overcome the problem of quantifying e.g. texture similarity of different camouflage nets to blend into the natural background, an image processing/visualization method was pursued by the Austrian Ministry of Defense. Now the same image-sets can be used for the human photo-simulation as well as for segmentation/classification by the camouflage assessment tool. Today a modified Euclid-distance measurement for visual images is being used while similarity of shapes (gestalt) to a selected region can be visualized. Feature selection is being done by training a neural network with the results of the human perception data. A cost effective prototype of a camouflage assessment tool based on standard hardware can be presented. Its promising performance gives hope to get beyond subjective camouflage experts stimuli. In the next project phase also thermal images shall be handled with the camouflage assessment tool.
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.