Fusing high-resolution SAR and optical imagery for improved urban land cover study and classification

Abstract

The two objectives of this study are to compare the performances of different data fusion techniques for the enhancement of urban features and subsequently to improve urban land cover types classification using a refined Bayesian classification. For the data fusion, wavelet-based fusion, Brovey transform, Elhers fusion and principal component analysis are used and the results are compared. The refined Bayesian classification uses spatial thresholds defined from local knowledge and different features obtained through a feature derivation process. The result of the refined classification is compared with the results of a standard method and it demonstrates a higher accuracy. Overall, the research indicates that multi-source information can significantly improves the interpretation and classification of land cover types and the refined Bayesian classification is a powerful tool to increase the classification accuracy.

Keywords:

• data fusion
• refined Bayesian classification
• multi-source
• urban
• feature derivation

Acknowledgements

A part of this research was conducted under the sponsorship for Humboldt Fellows. The authors also gratefully acknowledge the free TerraSAR data provided by Infoterra GmbH as part of a joint research project between ITC and Mongolia.