6533b82efe1ef96bd1293082
RESEARCH PRODUCT
Training label cleaning with ant colony optimization for classification of remote sensing imagery
Elena-catalina NeghinaVictor-emil Neagoesubject
Support vector machineTraining setComputer sciencebusiness.industryAnt colony optimization algorithmsArtificial intelligenceMachine learningcomputer.software_genrebusinesscomputerClassifier (UML)Remote sensingdescription
This paper presents an original approach for improving performances of the supervised classifiers in remote sensing imagery by proposing a technique to refine a given training set using Ant Colony Optimization (ACO). The new method called ACO-Training Label Cleaning (ACO-TLC) applies ACO model for selection of the significant training samples from a given set of labeled vectors in order to optimize the quality of a supervised classifier. This means to retain the most informative samples and to remove the uncertain or misclassified training samples, which lead to classification errors. As a result of the selection process, we can obtain a purified training set. The proposed model is implemented and evaluated using a LANDSAT 7 ETM+ image. The experimental results confirm the effectiveness of the proposed approach.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2015-07-01 | 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) |