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The aim of this paper is to give a homogeneous and a simple framework in order to present information fusion concepts. The main concept presented here concerns the definition of the information element concept. This concept is then illustrated through the general scheme of pattern recognition systems. Different types of information imperfection are then illustrated. Finally, information fusion concepts and fusion architecture are illustrated.

In the field of pattern recognition from satellite images, the existing road extraction methods have been either too specialized or too time consuming. The challenge then has been to develop a general and close to real time road extraction method. This study falls in this perspective and aims at developing a close to real time semi-automatic system able to extract linear planimetric features (including roads). The major concern of this study is to combine the most efficient tools to deal with the road primitive extraction process in order to handle multi- resolution and multi-type raw images. Hence, this study brought along a new model fusion characterized by the combination of operator input points (in 2D or 3D), fuzzy image filtering, cubic natural splines and the A*algorithm. First, a cubic natural splines interpolation of the operator points is used to parameterize the A*algorithm. Cost function with the consequence to restrict the mining research area. Second, the heuristic function of the same algorithm is combined with the fuzzy filtering which proves to be a fast and efficient tool for selection of the primitive most promising points. The combination of the cost function and the heuristic function leads to a limited number of hypothetical paths, hence decreasing the computation time. Moreover, the combination of the A*algorithm and the splines leads to a new way to solve the perceptual grouping problems. Results related to the problem of feature discontinuity suggest new research perspectives in relation to noisy area (urban) as well as noisy data (radar images).

The main topic of this study concerns edge detection using information fusion approaches. Edge detection methods are based on first and second order local operations followed by a thresholding and edge tracking techniques. In this study, an intermediate fuzzy-evidential conceptual level is introduced between the gray level and edge detection symbolic information level. From the image, evidences concerning edges and regions are extracted using fuzzy membership functions as well as contextual information. The proposed approach can be decomposed into two steps: (1) application of evidential reasoning approach in order to compute a basic masse function, (2) edge detection process based on the use of an iterative algorithm, exploiting the contextual information and a belief masse function. Masse function computation is based on the use of edge and region fuzzy membership functions of each pixel in the analyzed scene. The main interest of this step is to consider membership functions as being observed evidences instead of image gray level values. The key idea of the second step is to use all the information about regions, edges and contextual data in the edge extraction process. Obtained results are encouraging and the proposed methodology is shown to be robust to different noisy environments.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The availability of multi-sensed data, especially in remote sensing, leads to new possibilities in the area of target recognition. In fact, the information contained in an individual sensor represents only one facet of the reality. The use of several sensors aims at covering different facets of real world objects. In this study, the targets to recognize are the planimetric features (i.e. roads, energy transmission lines, railroads and rivers). The sensors used are visible type satellite sensors (SPOT Panchromatic and Landsat TM) as well as radar satellites (Radarsat fine mode and ERS-1). Sensor resolutions range from 8 to 30 meters/pixel. In this study, the modeling is not limited, as it is generally the case, to the problem feature's reality, but to each sensor that will be used. Moreover, the decision space (here a 3D symbolic map) has to be modeled in the same way as the reality and sensors to lead to a coherent and uniform system. Each model is developed using an object- oriented approach. Each reality-object is defined through its radiometric, geometric and topologic feature. The sensor model objects are defined in accordance to image acquisition and definition, including the stereo image cases (for SPOT and Radarsat). Finally, the decision space objects define the resulting 3D symbolic map where, for instance, a pixel attributes contain classification information as well as position, accuracy, reality object's membership values, etc.

Intelligent systems have to deal with expert's knowledge. Procedural representation is one of the most commonly used. The"/-x then y" rules formalism is a part ofthis category. The event x is generally called observation. In a multi-sensor context,a single observation generally represents one point of view. As sensors can be concurrent or complementary, one should beable, at the end, to qualify the reliability of this observation. Certainty theory introduces the concept of confidence factors (CF) for rule's premise. CF can be used to represent the confidence in an observation. These CF have two major weaknesses. First, as the rules are generally issued from experts, uncertainty should be related to their CF attribution and totheir ability to discriminate hypotheses. Second, CF are not suited for multi-sensor cases (i.e. no method is defmed tocombine efficiently single observation's CF taken from multiple sources).This study suggests a slight variation of the Dempster-Shafer theory using observation qualification in multi-sensorcontexts. The uncertainty is placed on the rules instead of on sources. Thus, sensor's specialization is taken into account. Bythis approach, the masses are not directly attributed on the frame of discernment elements, but on the rules themselves thatbecome the sources of knowledge, in the context of Dempster combining rule. It proposes then an approach for observationqualification in a multi-sensor context, as well as it suggests a new path for the delicate task ofmass attribution.Keywords: Fusion; Dempster-Shafer; observation confidence; uncertainty; remote sensing