Journal
INTERNATIONAL JOURNAL OF REMOTE SENSING
Volume 39, Issue 9, Pages 2784-2817Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/01431161.2018.1433343
Keywords
Land-cover classification; image classification; land-cover mapping; machine learning
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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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