SAR image interpretation based on Markov mesh random fields models

Author

Smits, P.C. ; Giorgini, F. ; Petrou, M. ; Dellepiane, Silvana G.

Author_Institution

Dept. of Biophys. & Electron. Eng., Genoa Univ.

Volume

2

fYear

1997

fDate

3-8 Aug 1997

Firstpage

737

Abstract

An unsupervised segmentation approach is presented for the analysis of intensity synthetic aperture radar (SAR) images. It utilizes hidden Markov mesh random fields (HMMRF), assuming that the image elements have a causal relationship: a pixel´s past is considered more important than its future, a hypothesis that is of particular interest to SAR images, given the way these images are generated. The approach is build upon the HMMRF theory of Devijver (1993), substituting his Gaussian distribution with the Gamma distribution, using an new initial image segmentation algorithm. The method is suited for real time image analysis, making the method powerful in, for instance, SAR image interpretation and data base browsing

Keywords

Gaussian distribution; gamma distribution; geophysical signal processing; geophysical techniques; hidden Markov models; image segmentation; radar imaging; remote sensing by radar; synthetic aperture radar; Gaussian distribution; Markov mesh random fields model; SAR image interpretation; algorithm; gamma distribution; geophysical measurement technique; hidden Markov mesh; image segmentation; land surface; radar image processing; radar remote sensing; real time image analysis; synthetic aperture radar; terrain mapping; unsupervised segmentation; Data analysis; Gaussian distribution; Hidden Markov models; Image analysis; Image generation; Image segmentation; Mesh generation; Pixel; Remote sensing; Synthetic aperture radar;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing, 1997. IGARSS '97. Remote Sensing - A Scientific Vision for Sustainable Development., 1997 IEEE International

Print_ISBN

0-7803-3836-7

Type

conf

DOI

10.1109/IGARSS.1997.615241

Filename

615241