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Conference Paper: A manifold learning approach of land cover classification for optical and SAR fusing data

TitleA manifold learning approach of land cover classification for optical and SAR fusing data
Authors
KeywordsLand cover classification
Dimension reduction
Optical and SAR data
Manifold learning
Issue Date2018
Citation
International Geoscience and Remote Sensing Symposium (IGARSS), 2018, v. 2018-July, p. 3567-3570 How to Cite?
Abstract© 2018 IEEE In the field of remote sensing, data acquired from a single sensor usually can't meet the needs of some special applications, because the information extracted from the data are often incomplete and limited. Data fusing can solve this problem, but it will lead to the redundant information. In this paper, we proposed a novel manifold learning approach to perform dimensionality reduction for the fusing optical and SAR data. And three typical manifold learning models, namely, ISOMAP, local linear embedding (LLE) and principle component analysis (PCA), were utilized to test the robustness of our method by comparing with the land cover classification results. Our experimental results showed that our proposed method obtained the best land cover classification results among these approaches for the fusing optical and SAR data.
Persistent Identifierhttp://hdl.handle.net/10722/277703

 

DC FieldValueLanguage
dc.contributor.authorTan, Xiangyu-
dc.contributor.authorJiang, Shaobin-
dc.contributor.authorZheng, Zezhong-
dc.contributor.authorZhong, Pingchuan-
dc.contributor.authorZhu, Mingcang-
dc.contributor.authorHe, Yong-
dc.contributor.authorYu, Zhenlu-
dc.contributor.authorWang, Na-
dc.contributor.authorJiang, Ling-
dc.contributor.authorZhou, Guoqing-
dc.contributor.authorZhang, Hongsheng-
dc.contributor.authorLi, Jiang-
dc.date.accessioned2019-09-27T08:29:45Z-
dc.date.available2019-09-27T08:29:45Z-
dc.date.issued2018-
dc.identifier.citationInternational Geoscience and Remote Sensing Symposium (IGARSS), 2018, v. 2018-July, p. 3567-3570-
dc.identifier.urihttp://hdl.handle.net/10722/277703-
dc.description.abstract© 2018 IEEE In the field of remote sensing, data acquired from a single sensor usually can't meet the needs of some special applications, because the information extracted from the data are often incomplete and limited. Data fusing can solve this problem, but it will lead to the redundant information. In this paper, we proposed a novel manifold learning approach to perform dimensionality reduction for the fusing optical and SAR data. And three typical manifold learning models, namely, ISOMAP, local linear embedding (LLE) and principle component analysis (PCA), were utilized to test the robustness of our method by comparing with the land cover classification results. Our experimental results showed that our proposed method obtained the best land cover classification results among these approaches for the fusing optical and SAR data.-
dc.languageeng-
dc.relation.ispartofInternational Geoscience and Remote Sensing Symposium (IGARSS)-
dc.subjectLand cover classification-
dc.subjectDimension reduction-
dc.subjectOptical and SAR data-
dc.subjectManifold learning-
dc.titleA manifold learning approach of land cover classification for optical and SAR fusing data-
dc.typeConference_Paper-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1109/IGARSS.2018.8517967-
dc.identifier.scopuseid_2-s2.0-85063125927-
dc.identifier.volume2018-July-
dc.identifier.spage3567-
dc.identifier.epage3570-

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