Texture characterization via sub-band predictive models

Author

Stan, S. ; Datcu, M.

Author_Institution

Remote Sensing Data Centre, German Aerosp. Centre, Wessling, Germany

Volume

1

fYear

1999

fDate

1999

Firstpage

342

Abstract

Textural information can efficiently describe the content of a remote sensing image. In this paper we present the characterization of textures via a conditional probability model for the magnitudes of the wavelet coefficients. We select the optimal model complexity using the Bayesian formalism. The order of the model and the associated model parameters can be used for texture segmentation and classification, or as an index in an image database for content-based retrieval

Keywords

Bayes methods; computational complexity; geophysical signal processing; image texture; remote sensing; wavelet transforms; Bayesian formalism; classification; conditional probability; content; content-based retrieval; image database; index; optimal model complexity; remote sensing image; sub-band predictive models; texture characterization; texture segmentation; wavelet coefficient; Bayesian methods; Content based retrieval; Image databases; Image retrieval; Image segmentation; Indexes; Information retrieval; Predictive models; Remote sensing; Wavelet coefficients;

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.1999.773492

Filename

773492