Forest detection and classification in tropical regions is very important for climate change research. Combining available data from different sensors is widely used in remote sensing to improve detection and classification performance. In this study, a decision fusion strategy is proposed to integrate optical and multifrequency PolSAR data for classification of rural areas including forest. Developed decision fusion strategy was validated with testing and validation samples which were manually selected from the high resolution satellite imagery. A total of three different sensor-originated scenes acquired on May 2010 in the Northwest of Tanzania were used in forest detection and classification experiments. The results show that combining classifiers for combinations of different sensor-originated features improves classification results for detailed class categories. Features which are properly modeled with the same statistical distribution are grouped and processed together. Classification results are weighted by using a reliability measure which is derived from confusion matrix of validation set. Therefore proposed decision fusion strategy improves the performance of parametric classifiers for some cases.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Decision fusion of classifiers for multifrequency PolSAR and optical data classification


    Contributors:


    Publication date :

    2013-06-01


    Size :

    1311845 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Agriculture Classification Using POLSAR Data

    Skriver, H. / Dall, J. / Le Toan, T. et al. | British Library Conference Proceedings | 2005


    Classification of Simple Vegetation Types Using POLSAR Image Data

    Freeman, A. / Electromagnetics Academy / United States; National Aeronautics and Space Administration et al. | British Library Conference Proceedings | 1993


    Generalized Wishart Mixtures for Unsupervised Classification of PoLSAR Data

    Li, L. / Chen, E. / Li, Z. et al. | British Library Conference Proceedings | 2013


    Generalized Wishart Mixtures for Unsupervised Classification of PolSAR Data

    Li, L. / Chen, E. / Li, Z. et al. | British Library Conference Proceedings | 2013