Multitemporal polarimetric RADARSAT-2 SAR data for urban land cover mapping through a dictionary-based and a rule-based model selection in a contextual SEM algorithm

This paper presents a dictionary- and rule-based model selection approach in an adaptive contextual semi-supervised algorithm for improving urban land cover classification using high-resolution multitemporal RADARSAT-2 polarimetric SAR (PolSAR) data. Six-date PolSAR data were acquired from June to September, 2008, over the Greater Toronto Area. Contextual information and the capabilities of different PolSAR distribution models were explored by the spatially variant Finite Mixture Model (FMM) with an adaptive Markov Random Field (MRF) in a Stochastic Expectation–Maximization (SEM) algorithm. This algorithm can obtain homogenous results while preserving shape details in the complex urban environment with high accuracy. Commonly used PolSAR distribution models such as Wishart, G0p, Kp, and KummerU were compared through the proposed approaches for urban land cover mapping. According to a Goodness-of-Fit test based on Mellin transformation, an accurate PolSAR distribution model could be selected with the dicti...