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 modelling 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.
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.
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 JR-signatures, there has been an increasing demand for analysing 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 IRsignatures 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.
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