K. Brady
Adaptive Probabilistic Models of Wavelet Packets for the Analysis and Segmentation of Textured Remote Sensing Images
Brady, K.; Jermyn, I.H.; Zerubia, J.
Authors
Contributors
Richard Harvey
Editor
Andrew Bangham
Editor
Abstract
Remote sensing imagery plays an important role in many fields. It has become an invaluable tool for diverse applications ranging from cartography to ecosystem management. In many of the images processed in these types of applications, semantic entities in the scene are correlated with textures in the image. In this paper, we propose a new method of analysing such textures based on adaptive probabilistic models of wavelet packets. Our approach adapts to the principal periodicities present in the textures, and can capture long-range correlations while preserving the independence of the wavelet packet coefficients. This technique has been applied to several remote sensing images, the results of which are presented.
Citation
Brady, K., Jermyn, I., & Zerubia, J. (2003). Adaptive Probabilistic Models of Wavelet Packets for the Analysis and Segmentation of Textured Remote Sensing Images. In R. Harvey, & A. Bangham (Eds.), Proceedings of the British Machine Conference 2003 (59.1-59.10). https://doi.org/10.5244/c.17.59
Conference Name | British Machine Vision Conference (BMVC) 2003 |
---|---|
Conference Location | Norwich |
Publication Date | Sep 1, 2003 |
Deposit Date | Aug 12, 2011 |
Publicly Available Date | May 11, 2016 |
Pages | 59.1-59.10 |
Book Title | Proceedings of the British Machine Conference 2003. |
DOI | https://doi.org/10.5244/c.17.59 |
Public URL | https://durham-repository.worktribe.com/output/1694263 |
Files
Accepted Conference Proceeding
(671 Kb)
PDF
You might also like
Modality-Constrained Density Estimation via Deformable Templates
(2021)
Journal Article
Assessing the Non-Uniqueness of a Well Test Interpretation Model Using a Bayesian Approach
(2020)
Conference Proceeding
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search