Training data in satellite image classification for land cover mapping: a review
The current land cover (LC) mapping paradigm relies on automatic satellite imagery classification, predominantly through supervised methods, which depend on training data to calibrate classification algorithms. Hence, training data have a critical influence on classification accuracy. Although resea...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | European Journal of Remote Sensing |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/22797254.2024.2341414 |
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| author | Daniel Moraes Manuel L. Campagnolo Mário Caetano |
| author_facet | Daniel Moraes Manuel L. Campagnolo Mário Caetano |
| author_sort | Daniel Moraes |
| collection | DOAJ |
| description | The current land cover (LC) mapping paradigm relies on automatic satellite imagery classification, predominantly through supervised methods, which depend on training data to calibrate classification algorithms. Hence, training data have a critical influence on classification accuracy. 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 followed, 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, sample 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. |
| format | Article |
| id | doaj-art-a647b71059e346e79587bd0959aca73d |
| institution | OA Journals |
| issn | 2279-7254 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | European Journal of Remote Sensing |
| spelling | doaj-art-a647b71059e346e79587bd0959aca73d2025-08-20T02:33:44ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542024-12-0157110.1080/22797254.2024.2341414Training data in satellite image classification for land cover mapping: a reviewDaniel Moraes0Manuel L. Campagnolo1Mário Caetano2NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Lisbon, PortugalForest Research Centre, School of Agriculture, University of Lisbon, Lisbon, PortugalNOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Lisbon, PortugalThe current land cover (LC) mapping paradigm relies on automatic satellite imagery classification, predominantly through supervised methods, which depend on training data to calibrate classification algorithms. Hence, training data have a critical influence on classification accuracy. 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 followed, 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, sample 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.https://www.tandfonline.com/doi/10.1080/22797254.2024.2341414Land coversatellite imagessupervised classificationtraining datasampling designsample quality |
| spellingShingle | Daniel Moraes Manuel L. Campagnolo Mário Caetano Training data in satellite image classification for land cover mapping: a review European Journal of Remote Sensing Land cover satellite images supervised classification training data sampling design sample quality |
| title | Training data in satellite image classification for land cover mapping: a review |
| title_full | Training data in satellite image classification for land cover mapping: a review |
| title_fullStr | Training data in satellite image classification for land cover mapping: a review |
| title_full_unstemmed | Training data in satellite image classification for land cover mapping: a review |
| title_short | Training data in satellite image classification for land cover mapping: a review |
| title_sort | training data in satellite image classification for land cover mapping a review |
| topic | Land cover satellite images supervised classification training data sampling design sample quality |
| url | https://www.tandfonline.com/doi/10.1080/22797254.2024.2341414 |
| work_keys_str_mv | AT danielmoraes trainingdatainsatelliteimageclassificationforlandcovermappingareview AT manuellcampagnolo trainingdatainsatelliteimageclassificationforlandcovermappingareview AT mariocaetano trainingdatainsatelliteimageclassificationforlandcovermappingareview |