Urban areas characterization from polarimetric SAR images using Hidden Markov Model

Scatterers in synthetic aperture radar (SAR) images exhibit high dependence on scatterer-sensor orientations. This phenomenon is prevalent in urban areas. This paper applies hidden Markov model (HMM) to characterize the dependence and model the variations with respect to orientation. Buildings in high resolution SAR images of urban areas are studied. Buildings regions are divided into several discrete classes according to their orientation angles. We model the variations of scatterers characteristics throughout the subapertures using HMM. Subapertures are generated using wavelet packet decomposition. The experimental results show that HMM is efficient in building detection and orientation angle identification. HMMs trained using different feature sets are investigated. The evolution of scatterer states in subapertures are obtained from the HMM inference.