In this paper, we report the results of some experiments on image classification and data fusion of remote sensing images, as part of ongoing efforts at the CVIP to develop a general strategy for the analysis of multimodality imaging. Statistical and Fuzzy logic approaches have been employed in these experiments. In all, six different algorithms for image classification, and an image fusion algorithm have been implemented and evaluated on common data sets. These algorithms are: (1) Supervised Parametric Bayes Classifier; (2) Non-parametric Bayesian Classifier using the Parzen density estimate; (3) Maximum a posteriori classification using the k-nearest neighbors (k-NN) approach; (4) MAP Estimation using Markov random field modeling; (5) a Fuzzy logic approach; and (6) a novel discriminate function classifier. The AMP segmentation of the regions in the image has been implemented using the Iterated Conditional Mode (ICM) optimization method. This approach provided the best results, in terms of the minimum probability of error and best reliability. A novel decision fusion algorithm, based on the a priori class conditional probability, has been applied to the classifiers' output.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Experiments in multimodality image classification and data fusion


    Contributors:
    Farag, A.A. (author) / Mohamed, R. (author) / Mahdi, H. (author)


    Publication date :

    2002-01-01


    Size :

    1518715 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Experiments in Multimodality Image Classification and Data Fusion

    Farag, A. / Mahdi, H. / Mohamed, R. et al. | British Library Conference Proceedings | 2002


    Multimodality

    Nobis, Claudia | Transportation Research Record | 2007



    3D multimodality medical image registration using morphological tools

    Maintz, J. B. / van den Elsen, P. A. / Viergever, M. A. | British Library Online Contents | 2001