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A frequent and quite normal situation in the crisis management is an instance of uncertainty and a lack of amount, structure, time adequacy and quality of information and data, thus the decisions makers and reacting managers have to deal with this problem. In the technologic project TP-06/0007-01 was implemented Decision Support System (DSS) for the area reduction for mine action management on a strategic level through the use of data fusion methodological approach and the upgrade of the results from SMART project . Although the DSS was applied on the problems of the humanitarian mine, it has wider potential and could be applied in other crisis situations. Data fusion methodological approach is based on (1) the interpreted and fused multi-sensor imagery collected by aerial reconnaissance and surveillance, (2) enhanced and extracted indicators of mine presence and indicators of mine absence, (3) the formalized expert knowledge, (4) on the combined contextual data, (5) the spatial data provided by geographic information system (GIS) and (6) on the data contained in other information systems (e.g. Mine Information System (MIS) of the mine action centre (MAC)). The generic solutions from SMART have been integrated with data fusion methodological approach and implemented in the form of the DSS. SMART is intended to provide some of this additional information that would help in two ways: it can reinforce the suspicion of some places and reduce the suspected area on others. Output of the DSS at the final stage consist of three maps: (1) the map of mine danger measure, (2) confidence map of indicators of mine presence and indicators of mine absence, (3) the map of spatial stability of results. The methodological combination of three of them enables the validation or dismissal of areas proposed for reduction, thus they are suitable for practical use in mine action management of the humanitarian demining.

Recent research in projects ARC and SMART (European Commission IST programme) shows that long wave thermal IR images enable detection of the objects that are otherwise not detectable. However, the important conclusion from this research is that the spatial resolution of available thermal IR cameras is not satisfactory. The possible way to compensate partially this deficiency can be data fusion. Due to the experience gained and the lessons learned in many years of the airborne remote sensing with thermal IR cameras we decided to analyze the fusion of the thermal IR images with images from visible and near IR wavelengths. The first goal was improvement of spatial resolution (sharpening) and this has been achieved. More important goal was the evaluation the radiometric quality of the fusion due to different physical background of the images. For this purpose many statistical parameters of different objects in the source thermal IR image were analysed and compared to the respective statistical parameters set for the same objects in the image obtained by fusion. The objects used in the analysis selected by use of the ground truth data that were available for the identification. The thermal IR contrast C between object “o” and background “b” used for comparison of the fused and source images too. The results of the analysis approved that the applied fusion method provides improvement of the spatial resolution and that thermal information of the analyzed objects conserved. Due to ap-proved quality of the fusion method, we recommend its application.

Airborne acquisition of the same part of terrain was done from different heights (500 and 900 meters), by digital matrix camera which collects data about landscape in four wavelengths: three visible channels (V: 0.4-0.5 µm, 0.5-0.6 µm, 0.6-0.7µm) and one near infrared channel (NIR: 0.7-0.9 µm). The set of images of the same terrain which are chosen consists of two RGB images (they are made of: green (G), red (R) and infrared (IR) = color infrared (CIR)) collected from: 500 m (higher space resolution) and 900 m (lower space resolution). With data fusion (used methods are: proncipal components, IHS and method of relative contribution) two products were made: RGB image with higher space resolution inside of the mentioned set of images (higher space resolution (from image collected from 500 m) was implemented in the image collected from 900 m) and RGB image with lower space resolution inside of the set of images (lower space resolution (from image collected from 900 m) was implemented in image collected from 500 m). This action produced two pairs of comparable images: the first one – the product of data fusion with higher spatial resolution and original image with higher spatial resolution (image collected from 500 m) ; and the second product of data fusion with lower spatial resolution and original image with lower resolution (image collected from 900 m). Namely, the main problem in the analysis of the quality of fused product is how to get reference image. In the majority cases that image needs to be produced, which means that both images are produced (result of fusion and reference image). On the contrary, in this work we provide the analysis of the qualitiy of fused products (on pixel level) on the existing original image basis. Those images are images collected from 500 and 900 m. Changes of images in both directions were monitored, regarding the improving and degradation of spatial resolution. The analysis of qualitiy of fused products was done through statistical parameters (mean, standard deviation, variance, corelation, scatter plot), contrast of images. At the end the existing proposition for protocol for assessment of the quality of fused product basis (Thomas& Wald, 2004) is discussed.

The information collected through the airborne remote sensing are useful for many scientific and practical works. In order to use collected information and existing knowledge of the observed object in the most efficient way for the purpose of solving specific problems, we have to examine their characteristics and if possible fuse them. This article is motivated by the existence of two operative digital sensor systems in Croatia that collect information at different wavelengths and with different methods of taking images. The first sensor is the digital matrix (staring) camera, for three visible channels (V: 0.4–0.5 μm, 0.5-0.6 μm, 0.6-0.7 μm), for near infrared channel (NIR: 0.7-1.0 μm) and the second sensor is the longwave thermal infrared (TIR: 8-14 μm) parallel scan camera. This paper will analyze the possibility of fusing information provided by above sensors, of one not very accessible area (mountainous terrain, with large differences of heights). Digital sensor systems yield multispectral images with different spatial and radiometric resolutions, geodetic and photogrammetric data (geographic maps, GPS data, ortophoto maps, geocoded mosaics), the insight in the field situation (ground truth) and other accessible sources provide inputs for fusion. It will also consider influence of the fusion on the results of classification of mosaics developed on multi-sensor, multi-spectral and multi-resolution digital images from above mentioned two sensor systems. The mosaics made on the basis of the above-mentioned sensor systems have been separately made, and later on the multi-spectral mosaics containing the information from all 5 channels will be created. Spatial and geometrical quality of multi-sensor and multi-spectral images and mosaics were analysed in earlier works, so this article will not deal with this matter, but will use the results of those earlier analyses. The work is an integral part of the research conducted in the scientific project ARC funded by the European Commission.