6533b82efe1ef96bd12926be

RESEARCH PRODUCT

A genetic algorithm approach to purify the classifier training labels for the analysis of remote sensing imagery

Elena-catalina NeghinaVictor-emil NeagoeVlad Chirila-berbentea

subject

Sample selectionSupport vector machineTraining set020204 information systemsGenetic algorithm0211 other engineering and technologies0202 electrical engineering electronic engineering information engineering02 engineering and technologyClassifier (UML)021101 geological & geomatics engineeringRemote sensing

description

This paper proposes a Genetic Algorithm (GA) approach to clean a given classifier training set for remote sensing image analysis. Starting from an initial set of training data, the new method called GA-Training Label Purifying (GA-TLP) consists of the significant training sample selection using GAs in order to maximize the classifier accuracy. This means to retain the most informative samples and to remove the uncertain, redundant, and misclassified ones. 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.

yearjournalcountryeditionlanguage
2017-07-012017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
https://doi.org/10.1109/igarss.2017.8127686