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A Novel Approach to Multimedia Ontology Engineering for Automated Reasoning over Audiovisual LOD Datasets Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Recursive Ensemble Land Cover Classification with Little Training Data and Many Classes

Authors : Yu Oya, Katsutoshi Kanamori, Hayato Ohwada

Published in: Intelligent Information and Database Systems

Publisher: Springer Berlin Heidelberg

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Abstract

Land-cover classification can construct a land-use map to analyze satellite images using machine learning. However, supervised machine learning requires a lot of training data since remote sensing data is of higher resolution that reveals many features. Therefore, this study proposed a method to generate self-training data from a small amount of training data. This method generates self-training, which is regarded as the correct class to consider various times and the surrounding land cover. As a result of self-training conducted using this method, the Kappa coefficient was 0.644 for 12 classification problems with one training data per class.

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Metadata
Title
Recursive Ensemble Land Cover Classification with Little Training Data and Many Classes
Authors
Yu Oya
Katsutoshi Kanamori
Hayato Ohwada
Copyright Year
2016
Publisher
Springer Berlin Heidelberg
Book
Intelligent Information and Database Systems

Print ISBN: 978-3-662-49380-9

Electronic ISBN: 978-3-662-49381-6

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-662-49381-6_50

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