Multiple Resolution Segmentation of Textured Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mathematical Techniques in Multisensor Data Fusion
Mathematical Techniques in Multisensor Data Fusion
Statistical modelling of multimodal SAR images
International Journal of Remote Sensing
IEEE Transactions on Image Processing
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The estimation of Mass functions is a key issue in evidence theory. In this paper, we propose an algorithmic framework to achieve this task using a statistical modeling of the data. The confidence level of each component in the frame of discernment is represented and quantified using a sub-mixture model, where each data cluster is approximated by a Dirichlet distribution. We discuss and show the interest of using the Dirichlet distribution to model sensors corrupted by non-Gaussian noise. The contextual relationship is integrated within the fusion scheme using Markov fields. In this context, we propose an adaptation of the iterated conditional modes (ICM) algorithm which permits to deal with compound hypotheses as defined by Dempster-Shafer theory. The experiments are conducted, in the context of image segmentation using multiple sensors, on synthetic, radar and optical (SPOT) images.