Implementation of machine-learning classification in remote sensing: an applied review

Machine learning offers the potential for effective and efficient classification of remotely sensed imagery. The strengths of machine learning include the capacity to handle data of high dimensionality and to map classes with very complex characteristics. Nevertheless, implementing a machine-learnin...

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Veröffentlicht in:International journal of remote sensing Jg. 39; H. 9; S. 2784 - 2817
Hauptverfasser: Maxwell, Aaron E., Warner, Timothy A., Fang, Fang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Taylor & Francis 03.05.2018
Taylor & Francis Ltd
Schlagworte:
Artificial intelligence
Artificial neural networks
Capacity
Classification
Data
data collection
Data requirements
decision support systems
Decision trees
Image classification
Imagery
Land-cover classification
land-cover mapping
Machine learning
Mathematical models
Neural networks
Remote sensing
Support vector machines
Training
ISSN:0143-1161, 1366-5901, 1366-5901
Online-Zugang:Volltext
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Zusammenfassung:Machine learning offers the potential for effective and efficient classification of remotely sensed imagery. The strengths of machine learning include the capacity to handle data of high dimensionality and to map classes with very complex characteristics. Nevertheless, implementing a machine-learning classification is not straightforward, and the literature provides conflicting advice regarding many key issues. This article therefore provides an overview of machine learning from an applied perspective. We focus on the relatively mature methods of support vector machines, single decision trees (DTs), Random Forests, boosted DTs, artificial neural networks, and k-nearest neighbours (k-NN). Issues considered include the choice of algorithm, training data requirements, user-defined parameter selection and optimization, feature space impacts and reduction, and computational costs. We illustrate these issues through applying machine-learning classification to two publically available remotely sensed data sets.
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ISSN:0143-1161
1366-5901
1366-5901
DOI:10.1080/01431161.2018.1433343