Linear planimetric feature domains modeling for multi-sensors fusion in remote sensing
Résumé
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.