International Journal of Remote Sensing· 2018Q2· Review
Implementation of machine-learning classification in remote sensing: an applied review
- 1,969citations
- Q2SCImago
- 2018year
Short summary
This review provides a practical overview of implementing machine learning for classifying remote sensing data, focusing on mature algorithms like SVMs, Random Forests, and neural networks.
AI-generated from the title and abstract; the full text is not read.
Key points
- Reviews mature machine learning algorithms for remote sensing classification (SVM, Random Forests, ANNs, k-NN).
- Addresses practical implementation challenges: algorithm selection, training data, parameter tuning, feature reduction.
- Illustrates issues with applications on two public remote sensing datasets.
- Highlights the potential of machine learning for high-dimensional and complex remote sensing data.
AI-generated from the title and abstract; the full text is not read.
Abstract
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.
The authors' abstract, as published at the source. International Journal of Remote Sensing, 2018 · DOI ↗
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Field: Media Technology
Media TechnologyEngineering