There are very good automatic detection algorithms available to be used in an Automatic Target Recognition
applications. However they need lots of data for training the detector for the specific use, e.g., performing an inventory
of wild animals. Ongoing work use thermally correct infrared models of animals for training the detector because
collecting real images from these wild animals is too expensive if even possible. This paper describes the process of
designing a good IR model of the animals, and the validation process of the thermal model. Several animals are modeled
using RadThermIR to be used for training detection algorithms. Animal models are based on commercially available
CAD models and are initiated by temperature values from real IR measurements in several different weather conditions.
The modeling extends the available set of training images by introducing different weather conditions and different poses
of the animal. Fat and fur thickness of the animal is modeled with respect to climate and weather.
State of the art and coming hyperspectral optical sensors generate large amounts of data and automatic analysis is
necessary. One example is Automatic Target Recognition (ATR), frequently used in military applications and a coming
technique for civilian surveillance applications. When sensors communicate in networks, the capacity of the
communication channel defines the limit of data transferred without compression. Automated analysis may have
different demands on data quality than a human observer, and thus standard compression methods may not be optimal.
This paper presents results from testing how the performance of detection methods are affected by compressing input
data with COTS coders. A standard video coder has been used to compress hyperspectral data. A video is a sequence of
still images, a hybrid video coder use the correlation in time by doing block based motion compensated prediction
between images. In principle only the differences are transmitted. This method of coding can be used on hyperspectral
data if we consider one of the three dimensions as the time axis. Spectral anomaly detection is used as detection method
on mine data. This method finds every pixel in the image that is abnormal, an anomaly compared to the surroundings.
The purpose of anomaly detection is to identify objects (samples, pixels) that differ significantly from the background,
without any a priori explicit knowledge about the signature of the sought-after targets. Thus the role of the anomaly
detector is to identify "hot spots" on which subsequent analysis can be performed. We have used data from Imspec, a
hyperspectral sensor. The hyperspectral image, or the spectral cube, consists of consecutive frames of spatial-spectral
images. Each pixel contains a spectrum with 240 measure points. Hyperspectral sensor data was coded with hybrid
coding using a variant of MPEG2. Only I- and P- frames was used. Every 10th frame was coded as I frame. 14
hyperspectral images was coded in 3 different directions using x, y, or z direction as time. 4 different quantization steps
were used. Coding was done with and without initial quantization of data to 8 bbp. Results are presented from applying
spectral anomaly detection on the coded data set.
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
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.
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 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.
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.
The Empirical mode decomposition (EMD) is an adaptive decomposition of the data, as is the Wavelet packet best basis decomposition. This work present the first attempt to examining the use of EMD for image compression purposes. The Intrinsic Mode Function (IMF) and their Hilbert spectra are compared to the wavelet basis and the wavelet packet decompositions expanded in each of its best bases on the same data. By decomposing the signal into basis functions, the waveforms in the signal is represented by the basis and a set of decorrelated discrete values in a vector. A coding scheme is presented where the idea is to decompose the signal into its IMF:s where only the max and min values for each IMF is transmitted. The reconstruction of the IMF in the decoder is done with spline interpolation. We have in the two-dimensional EMD an adaptive image decomposition without the limitations from filter kernels or cost functions. The IMF:s are, in the two-dimensional case, to be seen as spatial frequency subbands, with various center frequency and bandwidth along the image.
This work investigates the wavelet packet transforms and its abilities to efficiently represent images. We are interested in the image compression approach of image processing. The wavelet packet basis selection algorithm finds spatial frequency resonance in the image. The different decomposition trees that represents the optimal basis for the triplet image, cost function and filter gives us a feeling of chaos but for compression applications it doesn't matter that there is no typical tree for a particular image or that there is no strong trend for a certain type of tree in combination with a fixed filter or fixed cost function. The most important measure in image coding applications is believed to be the cost for coding the transform coefficients, it is even more important than the cost for choosing the optimal basis. When measuring the cost for coding the coefficient matrix we realize that we are free to choose a cost function that gives us a nice decomposition tree together with a good filter. We simulate the coding cost by estimate the entropy of the coefficient matrix. Results are presented from tests where the images from the Brodatz texture set have been decomposed with different filters and different cost functions and we also present calculations of the decision rule to split or not to split the subband. With the knowledge of the mean and variance of the input signal, we can calculate the typical decomposition tree for the signal using different image models.
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.