The combination of polarimetric SAR with satellite SAR and optical data for classification of agricultural land

Abstract A multi-spectral SPOT image, polarimetric airborne SAR data as well as satellite based C-band SAR data have been used to perform classification of agricultural fields and areas occupied by forest and lake. Conventional Maximum Likelihood classification has been compared with classification incorporating a Gaussian mixture class model, as well as an algorithm based on multi-resolution structured data and sequential MAP (SMAP). The classification accuracies found were generally high, using combinations of sensors. It is found that multi polarization data gives invaluable information to be used in a classification scheme, a feature that can be exploited in future satellite sen sors, like for instance ASAR on board ENVISAT. The Gaussian mixture class model performed only slightly better than the conventional maximum likelihood algorithm, whereas the SMAP algorithm improved the classification results.