Name
Abstract | ||
|---|---|---|
This paper describes a new method for a feature-based supervised classification of multi-channel SAR data. Classic feature selection and classification methods are inadequate due to the diverse statistical distributions of the input features. A method based on logistic regression (LR) and multinomial logistic regression (MNLR) for separating different classes is therefore proposed. Both methods, LR and MNLR, are less dependent on the statistical distribution of the input data. A new spatial regularization method is also introduced to increase consistency of the classification result. The classification method was applied to a project on humanitarian demining in which the relevant classes were defined by experts of a mine action center. A ground survey mission collected learning and validation samples for each class. Results of the proposed classification methods are shown and compared to a maximum likelihood classifier. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1016/j.patrec.2005.08.006 | Pattern Recognition Letters |
Keywords | Field | DocType |
input data,sar image classification,sar image classication,proposed classification method,multi-channel sar image,logistic regression,classification method,new spatial regularization method,multi-channel sar,multinomial logistic regression,feature-based supervised classification,diverse statistical distribution,input feature,supervised feature-based classification,new method,classification result,maximum likelihood,statistical distribution,feature selection | Data mining,Classification rule,Pattern recognition,Feature selection,Multinomial logistic regression,Logistic model tree,Regularization (mathematics),Probability distribution,Artificial intelligence,Feature based,Logistic regression,Mathematics | Journal |
Volume | Issue | ISSN |
27 | 4 | Pattern Recognition Letters |
Citations | PageRank | References |
3 | 0.41 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dirk Borghys | 1 | 43 | 6.07 |
| Yann Yvinec | 2 | 3 | 0.75 |
| C. Perneel | 3 | 45 | 4.55 |
| Aleksandra Pizurica | 4 | 1238 | 102.29 |
| W. Philips | 5 | 7 | 0.83 |