Multitemporal Spaceborne Polarimetric SAR Data for Urban Land Cover Mapping

Abstract: Urban represents one of the most dynamic areas in the global change context. To support rational policies for sustainable urban development, remote sensing technologies such as Synthetic Aperture Radar (SAR) enjoy increasing popularity for collecting up-to-date and reliable information such as urban land cover/land-use. With the launch of advanced spaceborne SAR sensors such as RADARSAT-2, multitemporal fully polarimetric SAR data in high-resolution become increasingly available. Therefore, development of new methodologies to analyze such data for detailed and accurate urban mapping is in demand. This research investigated multitemporal fine resolution spaceborne polarimetric SAR (PolSAR) data for detailed urban land cover mapping. To this end, the north and northwest parts of the Greater Toronto Area (GTA), Ontario, Canada were selected as the study area. Six-date C-band RADARSAT-2 fine-beam full polarimetric SAR data were acquired during June to September in 2008. Detailed urban land covers and various natural classes were focused in this study. Both object-based and pixel-based classification schemes were investigated for detailed urban land cover mapping. For the object-based approaches, Support Vector Machine (SVM) and rule-based classification method were combined to evaluate the classification capacities of various polarimetric features. Classification efficiencies of various multitemporal data combination forms were assessed. For the pixel-based approach, a temporal-spatial Stochastic Expectation-Maximization (SEM) algorithm was proposed. With an adaptive Markov Random Field (MRF) analysis and multitemporal mixture models, contextual information was explored in the classification process. Moreover, the fitness of alternative data distribution assumptions of multi-look PolSAR data were compared for detailed urban mapping by this algorithm. Both the object-based and pixel-based classifications could produce the finer urban structures with high accuracy. The superiority of SVM was demonstrated by comparison with the Nearest Neighbor (NN) classifier in object-based cases. Efficient polarimetric parameters such as Pauli parameters and processing approaches such as logarithmically scaling of the data were found to be useful to improve the classification results. Combination of both the ascending and descending data with appropriate temporal span are suitable for urban land cover mapping. The SEM algorithm could preserve the detailed urban features with high classification accuracy while simultaneously overcoming the speckles. Additionally the fitness of the G0p and Kp distribution assumptions were demonstrated better than the Wishart one.