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21 December 2000 Unsupervised classification of radar images based on hidden Markov models and generalized mixture estimation
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Abstract
Due to the enormous quantity of radar images acquired by satellites and through shuttle missions, there is an evident need for efficient automatic analysis tools. This article describes unsupervised classification of radar images in the framework of hidden Markov models and generalised mixture estimation. In particular, we show that hidden Markov chains, based on a Hilbert-Peano scan of the radar image, are a fast and efficient alternative to hidden Markov random fields for parameter estimation and unsupervised classification. We also describe how the distribution families and parameters of classes with homogeneous or textured radar reflectivity can be determined through generalised mixture estimation. Sample results obtained on real and simulated radar images are presented.
© (2000) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Roger Fjortoft, Jean-Marc Boucher, Yves Delignon, Rene Garello, Jean-Marc Le Caillec, Henri Maitre, Jean-Marie Nicolas, Wojciech Pieczynski, Marc Sigelle, and Florence Tupin "Unsupervised classification of radar images based on hidden Markov models and generalized mixture estimation", Proc. SPIE 4173, SAR Image Analysis, Modeling, and Techniques III, (21 December 2000); https://doi.org/10.1117/12.410644
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