THEMATIC INFORMATION EXTRACTION IN A NEURAL NETWORK CLASSIFICATION OF MULTI-SENSOR DATA INCLUDING MICROWAVE PHASE INFORMATION.

摘要

Microwave data (ERS-1 and ERS-2) and optical data (SPOT-XS) were used for the classification of an area with different land use classes. Classifications were executed for the optical data alone and for a combination of the three data sets. Two classifiers, one based on the maximum likelihood algorithm and the other on a neural network approach, were applied. From the ERS tandem mode SAR data a coherence map was created and included in the classifications in the form of an additional dimension in the feature space. The accuracy and reliability of the four classifications are presented and the results discussed.