PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Detecting changes in polarimetric SAR data with content-based image retrieval.
Matthieu Molinier, Jorma Laaksonen, Yrjö Rauste and Tuomas Häme
In: IEEE International Geoscience And Remote Sensing Symposium, 23-27 Jul 2007, Barcelona, Spain.


In this study, we extended the potential of a Content-Based Image Retrieval (CBIR) system based on Self-Organizing Maps (SOMs), for the analysis of remote sensing data. A database was artificially created by splitting each image to be analyzed into small images (or imagelets). Content-based image retrieval was applied to fully polarimetric airborne SAR data, using a selection of polarimetric features. After training the system on this imagelet database, automatic queries could detect changes. Results were encouraging on airborne SAR data and may be more useful for spaceborne polarimetric data.

EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Machine Vision
Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:3646
Deposited By:Jorma Laaksonen
Deposited On:14 February 2008