Frank Cremer

Sensor fusion for antipersonnel landmine detection: a case study

In this paper the multi sensor fusion results obtained within the European research project GEODE are presented. The layout of the test lane and the individual sensors used are described. The implementation of the SCOOP algorithm improves the ROC curves, as the false alarm surface and the number of false alarms both are taken into account. The confidence grids, as produced by the sensor manufacturers, of the sensors are used as input for the different sensor fusion methods implemented. The multisensor fusion methods implemented are Bayes, Dempster-Shafer, fuzzy probabilities and rules. The mapping of the confidence grids to the input parameters for fusion methods is an important step. Due to limited amount of the available data the entire test lane is used for training and evaluation. All four sensor fusion methods provide better detection results than the individual sensors.

Comparison of vehicle-mounted forward-looking polarimetric infrared and downward-looking infrared sensors for landmine detection

This paper gives a comparison of two vehicle-mounted infrared systems for landmine detection. The first system is a down-ward looking standard infrared camera using processing methods developed within the EU project LOTUS. The second system is using a forward-looking polarimetric infrared camera. Feature-based classification is used for this system. With these systems data have been acquired simultaneously of different test lanes from a moving platform. The performance of each system is evaluated using a leave-one-out method. On the training set the polarimetric infrared system performs better especially for low false alarm rates. On the independent evaluation set the differences are much smaller. On the ferruginous soil test lane the down-ward looking system performs better at certain points whereas on the grass test lane the forward-looking system performs better at certain points.

Depth fusion for anti-personnel landmine detection

In this paper we introduce the concept of depth fusion for anti-personnel landmine detection. Depth fusion is an extension of common sensor-fusion techniques for landmine detection. The difference lies within the fact that fusion of sensor data is performed in different physical depth layers. In order to do so, it requires a sensor that provides depth information for object detections. Our ground-penetrating radar fulfills this requirement. Depth fusion is then taken as the combination of the output of sensor fusion of all layers. The underlying idea is that sensor fusion for the surface layer has a different weighing of the sensors when compared with the sensor fusion in the deep layers because of apparent sensor characteristics. For example, a thermal IR sensor hardly adds information to the sensor fusion in the deep layers. Furthermore, GPR has difficulties suppressing clutter in the surface layer. As such, the surface fusion should emphasize on the TIR sensor, whereas sensor fusion in the deep layers should have a higher weighing of the GPR. This a priori information can be made explicit by choosing for a depth-fusion approach. Experimental results form measurements at the TNO-FEL test facility are presented that validate our depth-fusion concepts.

Feature-based detection of landmines in infrared images

High detection performance is required for an operational system for the detection of landmines. Humanitarian de-mining scenarios, combined with inherent difficulties of detecting landmines on an operational (vibration, motion, atmosphere) as well as a scenario level (clutter, soil type, terrain), result in high levels of false alarms for most sensors. To distinguish a landmine from background clutter one or more discriminating object features have to be found. The research described here focuses on finding and evaluating one or more features to distinguish disk-shaped landmines from background clutter in infrared images. These images were taken under controlled conditions, with homogenous soil types. Two methods are considered to acquire shape-based features in the infrared imagery. The first method uses a variation of the Hough transformation to find circular shaped objects. The second method uses the tophat filter with a disk-shaped structuring element. Furthermore, Mahalanobis and Fisher based classifiers are used to combine these features.

Thermal infrared identification of buried landmines

This paper deals with a three-dimensional thermal model for landmine detection problems and an inverse problem for reconstructing the physical parameters of buried objects. Moreover, solutions are given for the estimation of the soil thermal diffusivity and meteorological parameters, needed for solving the inverse problem. The paper describes the main fundamental principles of thermal modelling for buried object identification and illustrates the results on data acquired from a real minefield, together with qualitative and quantitative results illustrating the validity of the model.

Towards an operational sensor-fusion system for anti-personnel landmine detection

To acquire detection performance required for an operational system for the detection of anti-personnel landmines, it is necessary to use multiple sensor and sensor-fusion techniques. This paper describes five decision-level sensor- fusion techniques and their common optimization method. The performance of the sensor-fusion techniques is evaluated by means of Receiver Operator Characteristics curves. These techniques are tested on an outdoor test facility. Three of four test lanes of this facility are used as training set and the fourth is used as evaluation set. The detection performance of naive Bayes, Dempster-Shafer, voting and linear discriminant are very similar on both the training and the evaluation set. This is probably caused by the flexibility of the sensor-fusion techniques resulting into similar optimal solutions independent of the fusion technique.

Stand-off Thermal IR Minefield Survey: System concept and experimental results

A detailed description of the CLEARFAST system for thermal IR stand-off minefield survey is given. The system allows (i) a stand-off diurnal observation of hazardous area, (ii) detecting anomalies, i.e. locating and searching for targets which are thermally and spectrally distinct from their surroundings, (iii) estimating the physical parameters, i.e. depth and thermal diffusivity, of the detected anomalies, and (iv) providing panoramic (mosaic) images indicating the locations of suspect objects and known markers. The CLEARFAST demonstrator has been successfully deployed and operated, in November 2004, in a real minefield within the United Nations Buffer Zone in Cyprus. The paper describes the main principles of the system and illustrates the processing chain on a set of real minefield images, together with qualitative and quantitative results.

Infrared polarisation measurements of targets and backgrounds in a marine environment

The infrared (IR) radiation emitted or reflected in an off- normal direction from a smooth surface is partially polarized. This principle can be used for enhanced discrimination of targets from backgrounds in a marine environment. It has been shown that (man-made) targets do not demonstrate a pronounced polarization effect when observed from near normal direction whereas the sea background radiation has a certain degree of polarization in slant observation path. A measurement setup has been constructed for collecting polarized IR imagery. This setup contains a rotating polarization filter that rotates synchronously with the frame sync of the camera. Either a long wave IR (LWIR) or a mid wave IR (MWIR) camera can be mounted behind the rotating polarization filter. The synchronization allows a sequence of images to be taken with a predefined constant angle of rotation between the images. Out of this image sequence three independent Stokes images are constructed, containing the normal intensity part, the vertical/horizontal polarization and the diagonal polarization. Up to 20 full linearly polarized images can be acquired per second. Measurements are taken at the North Sea coast with this setup. The recorded images are analyzed to determine the influence of polarization on the detection of small targets in such an environment. Furthermore differences between polarization contrasts in MWIR are analyzed.

Detectability of surface-laid landmines with a polarimetric IR sensor

Polarimetric scattering models are developed to predict the detectability of surface-laid landmines. A specular polarimetric model works well only under the condition that there is either no sunlight or the sun is not close to the specular reflection direction. Moreover, this model does not give insight why certain man-made objects like landmines give a higher polarimetric signature than natural background. By introducing a polarimetric bidirectional reflectance distribution function (BRDF) the specular model is extended. This new model gives a better prediction of the polarimetric signature and gives a close match to the measurements of landmines with different casings as well as the sand background. The model parameters indicate that the landmines have a lower surface roughness and a higher refractive index, which is the reason why these objects are detectable from the background based on their polarimetric signature.

Usage of polarization features of land mines for improved automatic detection

In this paper the landmine detection performance of an IR and a visual light camera both equipped with a polarization filter are compared with the detection performance of these cameras without polarization filters. Sequences of images have been recorded with a rotating polarization filter in front of the cameras.