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Article: Stacked Autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping

TitleStacked Autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping
Authors
Issue Date2016
Citation
International Journal of Remote Sensing, 2016, v. 37, n. 23, p. 5632-5646 How to Cite?
Abstract© 2016 Informa UK Limited, trading as Taylor & Francis Group. Land-cover mapping is an important research topic with broad applicability in the remote-sensing domain. Machine learning algorithms such as Maximum Likelihood Classifier (MLC), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest (RF) have been playing an important role in this field for many years, although deep neural networks are experiencing a resurgence of interest. In this article, we demonstrate early efforts to apply deep learning-based classification methods to large-scale land-cover mapping. Based on the Stacked Autoencoder (SAE), one of the deep learning models, we built a classification framework for large-scale remote-sensing image processing. We adjusted and optimized the model parameters based on our test samples. We compared the performance of the SAE-based approach with traditional classification algorithms including RF, SVM, and ANN with multiple performance analytics. Results show that the SAE classifier trained with an entire set of African training samples achieves an overall classification accuracy of 78.99% when assessed by test samples collected independently of training samples, which is higher than the accuracies achieved by the other three classifiers (76.03%, 77.74%, and 77.86% of RF, SVM, and ANN, respectively) based on the same set of test samples. We also demonstrated the advantages of SAE in prediction time and land-cover mapping results in this study.
Persistent Identifierhttp://hdl.handle.net/10722/296800
ISSN
2020 Impact Factor: 3.151
2020 SCImago Journal Rankings: 0.918
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLi, Weijia-
dc.contributor.authorFu, Haohuan-
dc.contributor.authorYu, Le-
dc.contributor.authorGong, Peng-
dc.contributor.authorFeng, Duole-
dc.contributor.authorLi, Congcong-
dc.contributor.authorClinton, Nicholas-
dc.date.accessioned2021-02-25T15:16:42Z-
dc.date.available2021-02-25T15:16:42Z-
dc.date.issued2016-
dc.identifier.citationInternational Journal of Remote Sensing, 2016, v. 37, n. 23, p. 5632-5646-
dc.identifier.issn0143-1161-
dc.identifier.urihttp://hdl.handle.net/10722/296800-
dc.description.abstract© 2016 Informa UK Limited, trading as Taylor & Francis Group. Land-cover mapping is an important research topic with broad applicability in the remote-sensing domain. Machine learning algorithms such as Maximum Likelihood Classifier (MLC), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest (RF) have been playing an important role in this field for many years, although deep neural networks are experiencing a resurgence of interest. In this article, we demonstrate early efforts to apply deep learning-based classification methods to large-scale land-cover mapping. Based on the Stacked Autoencoder (SAE), one of the deep learning models, we built a classification framework for large-scale remote-sensing image processing. We adjusted and optimized the model parameters based on our test samples. We compared the performance of the SAE-based approach with traditional classification algorithms including RF, SVM, and ANN with multiple performance analytics. Results show that the SAE classifier trained with an entire set of African training samples achieves an overall classification accuracy of 78.99% when assessed by test samples collected independently of training samples, which is higher than the accuracies achieved by the other three classifiers (76.03%, 77.74%, and 77.86% of RF, SVM, and ANN, respectively) based on the same set of test samples. We also demonstrated the advantages of SAE in prediction time and land-cover mapping results in this study.-
dc.languageeng-
dc.relation.ispartofInternational Journal of Remote Sensing-
dc.titleStacked Autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/01431161.2016.1246775-
dc.identifier.scopuseid_2-s2.0-84993997577-
dc.identifier.volume37-
dc.identifier.issue23-
dc.identifier.spage5632-
dc.identifier.epage5646-
dc.identifier.eissn1366-5901-
dc.identifier.isiWOS:000387560400008-
dc.identifier.issnl0143-1161-

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