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

Neagoe, Victor-Emil; Neghina, Catalina; Chirila-Berbentea, Vlad

doi:10.1109/igarss.2017.8127686

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2017

DOI: 10.1109/igarss.2017.8127686

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

Victor-Emil Neagoe

Catalina Neghina

Vlad Chirila-Berbentea

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Cited by 4 publications

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Supervized Change Detection for SAR Imagery Based on Processing of a Low Size Training Data Set by an Ensemble of Self-Organizing Maps

Neagoe

Ciotec

2018

2018 International Conference on Communications (COMM)

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

Cited by 4 publications

References 8 publications

Supervized Change Detection for SAR Imagery Based on Processing of a Low Size Training Data Set by an Ensemble of Self-Organizing Maps

Supervized Change Detection for SAR Imagery Based on Processing of a Low Size Training Data Set by an Ensemble of Self-Organizing Maps

A New Neural Approach of Supervised Change Detection in SAR Images Using Training Data Generation with Concurrent Self-Organizing Maps

A Novel Ant Colony Optimization Based Training Subset Selection Algorithm for Hyperspectral Image Classification

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