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Advisor(s)
Abstract(s)
The current land cover (LC) mapping paradigm relies on automatic satellite imagery classifica-
tion, predominantly through supervised methods, which depend on training data to calibrate
classification algorithms. Hence, training data have a critical influence on classification accu-
racy. Although research on specific aspects of training data in the LC classification context
exists, a study that organizes and synthetizes the multiplicity of aspects and findings of these
researches is needed. In this article, we review the training data used for LC classification of
satellite imagery. A protocol of identification and selection of relevant documents was fol-
lowed, resulting in 114 peer-reviewed studies included. Main research topics were identified
and documents were characterized according to their contribution to each topic, which
allowed uncovering subtopics and categories and synthetizing the main findings regarding
different aspects of the training dataset. The analysis found four research topics, namely
construction of the training dataset, sample quality, sampling design and advanced learning
techniques. Subtopics included sample collection method, sample cleaning procedures, sam-
ple size, sampling method, class balance and distribution, among others. A summary of the
main findings and approaches provided an overview of the research in this area, which may
serve as a starting point for new LC mapping initiatives.
Description
Keywords
Land cover satellite images supervised classification training data sampling design sample quality
Pedagogical Context
Citation
Daniel Moraes, Manuel L. Campagnolo & Mário Caetano (2024) Training data in satellite image classification for land cover mapping: a review, European Journal of Remote Sensing, 57:1, 2341414, DOI: https://doi.org/10.1080/22797254.2024.2341414
Publisher
Taylor & Francis