Recognition of occluded objects in synthetic aperture radar (SAR) images is a significant problem for automatic target recognition. In this paper, we present a hidden Markov modeling (HMM) based approach for recognizing objects in synthetic aperture radar (SAR) images. We identify the peculiar characteristics of SAR sensors and using these characteristics we develop feature based multiple models for a given SAR image of an object. The models exploiting the relative geometry of feature locations or the amplitude of SAR radar return are based on sequentialization of scattering centers extracted from SAR images. In order to improve performance we integrate these models synergistically using their probabalistic estimates for recognition of a particular target at a specific azimuth. Experimental results are presented using both synthetic and real SAR images.
Recognition of occluded targets using stochastic models
2000-01-01
370398 byte
Conference paper
Electronic Resource
English
Recognition of Occluded Targets Using Stochastic Models
British Library Conference Proceedings | 2000
|Partially Occluded Object Recognition Using Statistical Models
British Library Online Contents | 2002
|Robust Occluded Shape Recognition
British Library Conference Proceedings | 2006
|Recognition of occluded objects by reducing feature interactions
British Library Online Contents | 2012
|