Name
Title | ||
|---|---|---|
A cascaded ensemble classifier for object segmentation in high resolution polarimetric SAR data |
Abstract | ||
|---|---|---|
The paper proposes a novel approach to object classification and segmentation in multi-channel (e.g. polarimetric) SAR data. The classifier is intended for particularly difficult problems, where objects of interest exhibit a high degree of radiometric, polarimetric and geometric heterogeneity, both within individual object instances and across the object category as a whole. Classification is based on a non-parametric characterization of scene contents that avoids model assumptions liable to fail in this scenario. The classifier structure is based on a combination of techniques developed for related problems in computer vision: the cascade architecture helps breaking down the problem into manageable stages while random forests provide a powerful framework for learning and combining discriminative classification rules. In addition, scale space techniques explicitly introduce non-local, contextual and geometric information into the classification process. Preliminary results illustrate the potential of the proposed approach with respect to the task of building segmentation in dual-polarized TerraSAR-X data. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IGARSS.2014.6946603 | Geoscience and Remote Sensing Symposium |
Keywords | Field | DocType |
geometry,radar polarimetry,radiometry,synthetic aperture radar,vegetation,cascade architecture,cascaded ensemble classifier,classification process,classifier structure,computer vision problem,contextual information,discriminative classification rule,dual-polarized TerraSAR-X data segmentation,geometric heterogeneity,geometric information,high radiometric degree,high resolution polarimetric SAR data,individual object instance,manageable stage,model assumption,multichannel SAR data,nonlocal information,nonparametric scene content characterization,object category,object classification,object segmentation,polarimetric heterogeneity,powerful learning framework,random forest,scale space technique,technique combination,Classification,SAR Polarimetry,Synthetic Aperture Radar,Texture | Computer vision,Pattern recognition,Segmentation,Computer science,Remote sensing,Polarimetric sar,Artificial intelligence,Classifier (linguistics) | Conference |
ISSN | Citations | PageRank |
2153-6996 | 0 | 0.34 |
References | Authors | |
4 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marc Jäger | 1 | 116 | 9.88 |
| Andreas Reigber | 2 | 670 | 70.53 |
| Olaf Hellwich | 3 | 185 | 37.01 |