What question did this study set out to answer?

To review the practical application of mature machine-learning classification techniques in remote sensing and address contradictory guidance in the literature.

February 2, 2018International Journal of Remote SensingOpen Access

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

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To review the practical application of mature machine-learning classification techniques in remote sensing and address contradictory guidance in the literature.
Reviewed core algorithms: support vector machines, single decision trees, Random Forests, boosted decision trees, artificial neural networks, and k-nearest neighbours.
Evaluated operational factors including algorithm selection, training data requirements, parameter optimization, feature space reduction, and computational cost.
Demonstrated practical classification considerations by testing algorithms on two publicly available remote sensing datasets.
Highlighted operational trade-offs across algorithms, showing how user-defined parameter tuning and training data constraints directly impact classification fidelity.
Demonstrated that feature space reduction and optimization strategies manage high-dimensional data while controlling computational expense in satellite image classification.

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Implication

Review evaluates machine-learning algorithms across remote sensing datasets, highlighting key operational challenges in parameter selection, training requirements, and feature reduction.

Cite This Study

Maxwell et al. (2018) studied this question.

synapsesocial.com/papers/69d87c01d2f7327e70ae33c5 https://doi.org/10.1080/01431161.2018.1433343

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Implication

Review evaluates machine-learning algorithms across remote sensing datasets, highlighting key operational challenges in parameter selection, training requirements, and feature reduction.

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

Key Points

Discussion

Implication

Cite This Study

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

Key Points

Discussion

Implication

Cite This Study

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