Integration of multitemporal/polarization C-band SAR data sets for land-cover classification

This paper investigates the potential of multitemporal/polarization C-band SAR data for land-cover classification. Multitemporal Radarsat-1 data with HH polarization and ENVISAT ASAR data with VV polarization acquired in the Yedang plain, Korea are used for the classification of typical five land-cover classes in an agricultural area. The presented methodologies consist of two analytical stages: one for feature extraction and the other for classification based on the combination of features. Both a traditional SAR signal property analysis-based approach and principal-component analysis (PCA) are applied in the feature extraction stage. Special concerns are in the interpretation of each principal component by using principal-component loading. The tau model applied as a decision-level fusion methodology can provide a formal framework in which the posteriori probabilities derived from different sensor data can be combined. From the case study results, the combination of PCA-based features showed improved classification accuracy for both Radarsat-1 and ENVISAT ASAR data, as compared with the traditional SAR signal property analysis-based approach. The integration of PCA-based features based on multiple polarization (i.e. HH from Radarsat-1, and both VV and VH from ENVISAT ASAR) and different incidence angles contributed to a significant improvement of discrimination capability for dry fields which could not be properly classified by using only Radarsat-1 or ENVISAT ASAR data, and thus showed the best classification accuracy. The results of this case study indicate that the use of multiple polarization SAR data with a proper feature extraction stage would improve classification accuracy in multitemporal SAR data classification, although further consideration should be given to the polarization and incidence angle dependency of complex land-cover classes through more experiments.