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Article: Land-Use Mapping for High-Spatial Resolution Remote Sensing Image Via Deep Learning: A Review

TitleLand-Use Mapping for High-Spatial Resolution Remote Sensing Image Via Deep Learning: A Review
Authors
KeywordsDeep learning (DL)
high-spatial resolution remote sensing images (HSR-RSIs)
land-use mapping (LUM)
semantic segmentation
Issue Date2021
Citation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, v. 14, p. 5372-5391 How to Cite?
AbstractLand-use mapping (LUM) using high-spatial resolution remote sensing images (HSR-RSIs) is a challenging and crucial technology. However, due to the characteristics of HSR-RSIs, such as different image acquisition conditions and massive, detailed information, and performing LUM faces unique scientific challenges. With the emergence of new deep learning (DL) algorithms in recent years, methods to LUM with DL have achieved huge breakthroughs, which offer novel opportunities for the development of LUM for HSR-RSIs. This article aims to provide a thorough review of recent achievements in this field. Existing high spatial resolution datasets in the research of semantic segmentation and single-object segmentation are presented first. Next, we introduce several basic DL approaches that are frequently adopted for LUM. After reviewing DL-based LUM methods comprehensively, which highlights the contributions of researchers in the field of LUM for HSR-RSIs, we summarize these DL-based approaches based on two LUM criteria. Individually, the first one has supervised learning, semisupervised learning, or unsupervised learning, while another one is pixel-based or object-based. We then briefly review the fundamentals and the developments of the development of semantic segmentation and single-object segmentation. At last, quantitative results that experiment on the dataset of ISPRS Vaihingen and ISPRS Potsdam are given for several representative models such as fully convolutional network (FCN) and U-Net, following up with a comparison and discussion of the results.
Persistent Identifierhttp://hdl.handle.net/10722/329708
ISSN
2023 Impact Factor: 4.7
2023 SCImago Journal Rankings: 1.434
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZang, Ning-
dc.contributor.authorCao, Yun-
dc.contributor.authorWang, Yuebin-
dc.contributor.authorHuang, Bo-
dc.contributor.authorZhang, Liqiang-
dc.contributor.authorMathiopoulos, P. Takis-
dc.date.accessioned2023-08-09T03:34:45Z-
dc.date.available2023-08-09T03:34:45Z-
dc.date.issued2021-
dc.identifier.citationIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, v. 14, p. 5372-5391-
dc.identifier.issn1939-1404-
dc.identifier.urihttp://hdl.handle.net/10722/329708-
dc.description.abstractLand-use mapping (LUM) using high-spatial resolution remote sensing images (HSR-RSIs) is a challenging and crucial technology. However, due to the characteristics of HSR-RSIs, such as different image acquisition conditions and massive, detailed information, and performing LUM faces unique scientific challenges. With the emergence of new deep learning (DL) algorithms in recent years, methods to LUM with DL have achieved huge breakthroughs, which offer novel opportunities for the development of LUM for HSR-RSIs. This article aims to provide a thorough review of recent achievements in this field. Existing high spatial resolution datasets in the research of semantic segmentation and single-object segmentation are presented first. Next, we introduce several basic DL approaches that are frequently adopted for LUM. After reviewing DL-based LUM methods comprehensively, which highlights the contributions of researchers in the field of LUM for HSR-RSIs, we summarize these DL-based approaches based on two LUM criteria. Individually, the first one has supervised learning, semisupervised learning, or unsupervised learning, while another one is pixel-based or object-based. We then briefly review the fundamentals and the developments of the development of semantic segmentation and single-object segmentation. At last, quantitative results that experiment on the dataset of ISPRS Vaihingen and ISPRS Potsdam are given for several representative models such as fully convolutional network (FCN) and U-Net, following up with a comparison and discussion of the results.-
dc.languageeng-
dc.relation.ispartofIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing-
dc.subjectDeep learning (DL)-
dc.subjecthigh-spatial resolution remote sensing images (HSR-RSIs)-
dc.subjectland-use mapping (LUM)-
dc.subjectsemantic segmentation-
dc.titleLand-Use Mapping for High-Spatial Resolution Remote Sensing Image Via Deep Learning: A Review-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/JSTARS.2021.3078631-
dc.identifier.scopuseid_2-s2.0-85105886460-
dc.identifier.volume14-
dc.identifier.spage5372-
dc.identifier.epage5391-
dc.identifier.eissn2151-1535-
dc.identifier.isiWOS:000660636600008-

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