MULTISOURCE CLASSIFICATION OF REMOTELY-SENSED DATA - FUSION OF LANDSAT TM AND SAR IMAGES

We propose a new method for statistical classification of multisource data. The method is suited for land-use classification based on the fusion of remotely sensed images of the same scene captured at different dates from multiple sources. It incorporates a priori information about the likelihood of changes between the acquisition of the different images to be fused. A framework for the fusion of remotely sensed data based on a Bayesian formulation is presented. First, a simple fusion model is given, and then the basic model is extended to take into account the temporal attribute if the different data sources are acquired at different dates. The performance of the model is evaluated by fusing Landsat TM images and ERS-1 SAR images for land-use classification. The fusion model gives significant improvements in the classification error rates compared to the conventional single-source classifiers.