Name
Abstract | ||
|---|---|---|
This paper presents simple but powerful combination methods of dedicated one-class classifiers (OCCs) for efficient remote sensing image classification. The mean and product combination rules are applied to the probabilistic outputs generated by OCCs, and the performance is illustrated in a urban monitoring application in which multi-sensor (optical and SAR) data and multi-source (spectral and contextual) features are available. Two OCCs are used as core parts: the classical mixture of Gaussians (MoG) and the support vector domain description (SVDD) classifier. The obtained results by combining SVDD classifier outputs show a clear improvement in the accuracy, and more robustness to high dimensional samples compared to both MoG and stacked approaches. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/IGARSS.2007.4423095 | Barcelona |
Keywords | Field | DocType |
geophysical signal processing,geophysical techniques,image classification,remote sensing,support vector machines,synthetic aperture radar,Gaussian classical mixture,MoG,OCC probabilistic outputs,SAR data,SVDD classifier,contextual features,dedicated OCC combination methods,dedicated one class classifiers,efficient remote sensing image classification,mean combination rule,multisensor data,multisource features,one class remote sensing image classifiers,optical data,product combination rule,spectral features,support vector domain description,urban monitoring application | Data mining,Computer science,Synthetic aperture radar,Remote sensing,Robustness (computer science),Artificial intelligence,Probabilistic logic,Classifier (linguistics),Contextual image classification,Kernel (linear algebra),Pattern recognition,Support vector machine,Mixture model | Conference |
ISSN | ISBN | Citations |
2153-6996 | 978-1-4244-1212-9 | 4 |
PageRank | References | Authors |
0.43 | 8 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jordi Muñoz-Marí | 1 | 559 | 40.11 |
| Gustavo Camps-Valls | 2 | 2011 | 114.02 |
| L. G'omez-Chova | 3 | 181 | 13.79 |
| Javier Calpe-Maravilla | 4 | 92 | 11.69 |
| Camps-Valls, G. | 5 | 4 | 0.43 |
| Calpe-Maravilla, J. | 6 | 4 | 0.43 |