Fusion of multisource data sets from agricultural areas for improved land cover classification

An approach for spectral-spatial classification of multisource remote sensing data from agricultural areas is addressed. Mathematical morphology is used to derive the spatial information from the data sets. The different data sources (i.e., SAR and multispectral) are classified by support vector machines (SVM). Afterwards, the SVM outputs are transferred to probability measurements. These probability values are combined by different fusion strategies, to derive the final classification result. Comparing the results based on mathematical morphology the total accuracy increased by 6% compared to the pure-pixel classification results. Moreover the transfer of the SVM outputs into probability values and the subsequent fusion further increases the classification accuracy, resulting in an accuracy of 78.5%.