Less is more: optimizing classification performance through feature selection in a very-high-resolution remote sensing object-based urban application

Less is more: optimizing classification performance through feature selection in a very-high-resolution remote sensing object-based urban application

This study evaluates the impact of four feature selection (FS) algorithms in an object-based image analysis framework for very-high-resolution land use-land cover classification. The selected FS algorithms, correlation-based feature selection, mean decrease in accuracy, random forest (RF) based recu...

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Bibliographic Details
Main Authors: Stefanos Georganos, Tais Grippa, Sabine Vanhuysse, Moritz Lennert, Michal Shimoni, Stamatis Kalogirou, Eleonore Wolff
Format: Article
Language:English
Published: Taylor & Francis Group 2018-03-01
Series:GIScience & Remote Sensing
Subjects:
obia
land cover classification
extreme gradient boosting
feature selection
machine learning
Online Access:http://dx.doi.org/10.1080/15481603.2017.1408892

Internet

http://dx.doi.org/10.1080/15481603.2017.1408892

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