Bayesian modeling of remote sensing image content

Author

Schröder, Michael ; Seidel, Klaus ; Datcu, Mihai

Author_Institution

Commun. Technol. Lab., Swiss Fed. Inst. of Technol., Zurich, Switzerland

Volume

3

fYear

1999

fDate

1999

Firstpage

1810

Abstract

Remote sensing images exhibit an enormous amount of information. In order to extract this information in a robust way and to make it available as efficient indices for query by image content, the authors present a scheme of hierarchical stochastic description. The different levels in this hierarchy are derived from the different levels of abstraction: image data (0), image features (1), meta features (2), image classification (3), geometric features (4), and user-specific semantics (5). They describe this hierarchical scheme and the processes of Bayesian inference between these levels and present a case study using synthetic aperture radar (SAR) data

Keywords

Bayes methods; geophysical signal processing; geophysical techniques; image classification; radar imaging; remote sensing; remote sensing by radar; synthetic aperture radar; Bayes method; Bayesian inference; Bayesian modeling; SAR imagery; geometric feature; geophysical measurement technique; hierarchical stochastic description; hierarchy; image classification; image content; image data; image feature; image processing; land surface; meta feature; query; radar imaging; radar remote sensing; remote sensing; synthetic aperture radar; terrain mapping; user-specific semantics; Bayesian methods; Character generation; Communications technology; Data mining; Design for disassembly; Image classification; Remote sensing; Robustness; Stochastic processes; Synthetic aperture radar;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 1999. IGARSS '99 Proceedings. IEEE 1999 International

Conference_Location

Hamburg

Print_ISBN

0-7803-5207-6

Type

conf

DOI

10.1109/IGARSS.1999.772103

Filename

772103