View in EDS HTML Full Text PDF Full Text

Dual-Task Network for Terrace and Ridge Extraction: Automatic Terrace Extraction via Multi-Task Learning.

Saved in:
Bibliographic Details
Title: Dual-Task Network for Terrace and Ridge Extraction: Automatic Terrace Extraction via Multi-Task Learning.
Authors: Zhang, Jun1 (AUTHOR) fuhuyan@ynu.edu.cn, Huang, Xiao2 (AUTHOR) xiao.huang2@emory.edu, Zhou, Weixun3 (AUTHOR) zhouwx@nuist.edu.cn, Fu, Huyan1 (AUTHOR) chenyuyan@mail.ynu.edu.cn, Chen, Yuyan1 (AUTHOR) zhan-zhenghao@mail.ynu.edu.cn, Zhan, Zhenghao1 (AUTHOR)
Source: Remote Sensing. Feb2024, Vol. 16 Issue 3, p568. 22p.
Subjects: Deep learning, Terracing, Soil conservation, Data mining, Feature extraction, Remote sensing
Abstract: Terrace detection and ridge extraction from high-resolution remote sensing imagery are crucial for soil conservation and grain production on sloping land. Traditional methods use low-to-medium resolution images, missing detailed features and lacking automation. Terrace detection and ridge extraction are closely linked, with each influencing the other's outcomes. However, most studies address these tasks separately, overlooking their interdependence. This research introduces a cutting-edge, multi-scale, and multi-task deep learning framework, termed DTRE-Net, designed for comprehensive terrace information extraction. This framework bridges the gap between terrace detection and ridge extraction, executing them concurrently. The network incorporates residual networks, multi-scale fusion modules, and multi-scale residual correction modules to enhance the model's robustness in feature extraction. Comprehensive evaluations against other deep learning-based semantic segmentation methods using GF-2 terraced imagery from two distinct areas were undertaken. The results revealed intersection over union (IoU) values of 85.18% and 86.09% for different terrace morphologies and 59.79% and 73.65% for ridges. Simultaneously, we have confirmed that the connectivity of results is improved when employing multi-task learning for ridge extraction compared to directly extracting ridges. These outcomes underscore DTRE-Net's superior capability in the automation of terrace and ridge extraction relative to alternative techniques. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 175391473
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Dual-Task Network for Terrace and Ridge Extraction: Automatic Terrace Extraction via Multi-Task Learning.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Jun%22">Zhang, Jun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> fuhuyan@ynu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Huang%2C+Xiao%22">Huang, Xiao</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> xiao.huang2@emory.edu</i><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Weixun%22">Zhou, Weixun</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> zhouwx@nuist.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Fu%2C+Huyan%22">Fu, Huyan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> chenyuyan@mail.ynu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Yuyan%22">Chen, Yuyan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zhan-zhenghao@mail.ynu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhan%2C+Zhenghao%22">Zhan, Zhenghao</searchLink><relatesTo>1</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Feb2024, Vol. 16 Issue 3, p568. 22p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Terracing%22">Terracing</searchLink><br /><searchLink fieldCode="DE" term="%22Soil+conservation%22">Soil conservation</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Terrace detection and ridge extraction from high-resolution remote sensing imagery are crucial for soil conservation and grain production on sloping land. Traditional methods use low-to-medium resolution images, missing detailed features and lacking automation. Terrace detection and ridge extraction are closely linked, with each influencing the other's outcomes. However, most studies address these tasks separately, overlooking their interdependence. This research introduces a cutting-edge, multi-scale, and multi-task deep learning framework, termed DTRE-Net, designed for comprehensive terrace information extraction. This framework bridges the gap between terrace detection and ridge extraction, executing them concurrently. The network incorporates residual networks, multi-scale fusion modules, and multi-scale residual correction modules to enhance the model's robustness in feature extraction. Comprehensive evaluations against other deep learning-based semantic segmentation methods using GF-2 terraced imagery from two distinct areas were undertaken. The results revealed intersection over union (IoU) values of 85.18% and 86.09% for different terrace morphologies and 59.79% and 73.65% for ridges. Simultaneously, we have confirmed that the connectivity of results is improved when employing multi-task learning for ridge extraction compared to directly extracting ridges. These outcomes underscore DTRE-Net's superior capability in the automation of terrace and ridge extraction relative to alternative techniques. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=175391473
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/rs16030568
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 22
        StartPage: 568
    Subjects:
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Terracing
        Type: general
      – SubjectFull: Soil conservation
        Type: general
      – SubjectFull: Data mining
        Type: general
      – SubjectFull: Feature extraction
        Type: general
      – SubjectFull: Remote sensing
        Type: general
    Titles:
      – TitleFull: Dual-Task Network for Terrace and Ridge Extraction: Automatic Terrace Extraction via Multi-Task Learning.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Zhang, Jun
      – PersonEntity:
          Name:
            NameFull: Huang, Xiao
      – PersonEntity:
          Name:
            NameFull: Zhou, Weixun
      – PersonEntity:
          Name:
            NameFull: Fu, Huyan
      – PersonEntity:
          Name:
            NameFull: Chen, Yuyan
      – PersonEntity:
          Name:
            NameFull: Zhan, Zhenghao
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 02
              Text: Feb2024
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-print
              Value: 20724292
          Numbering:
            – Type: volume
              Value: 16
            – Type: issue
              Value: 3
          Titles:
            – TitleFull: Remote Sensing
              Type: main
ResultId 1