An MRF based framework integrating InSAR phase unwrapping and classification

Author

Smits, P.C. ; Dellepiane, S.G.

Author_Institution

Agric. Inf. Syst. Unit, Space Applications Inst., Ispra, Italy

Volume

1

fYear

1998

fDate

6-10 Jul 1998

Firstpage

168

Abstract

A generic theoretical framework is discussed for using different types of prior knowledge in a phase unwrapping approach based on a Markov random field (MRF) formalism. This approach allows for the regularization of the phase unwrapping process by using various types of information such as thematic maps and can help to make results more robust to disturbing phenomena like statistical noise and aliasing than classical approaches

Keywords

Markov processes; geophysical signal processing; geophysical techniques; image classification; radar imaging; remote sensing by radar; spaceborne radar; synthetic aperture radar; InSAR; Markov random field; geophysical measurement technique; image classification; interferometric SAR; land surface; phase unwrapping; prior knowledge; radar imaging; radar remote sensing; regularization; synthetic aperture radar; terrain mapping; theoretical framework; Agriculture; Bayesian methods; Information systems; Labeling; Layout; Markov random fields; Noise robustness; Phase noise; Probability density function; Synthetic aperture radar interferometry;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium Proceedings, 1998. IGARSS '98. 1998 IEEE International

Conference_Location

Seattle, WA

Print_ISBN

0-7803-4403-0

Type

conf

DOI

10.1109/IGARSS.1998.702840

Filename

702840