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Analysis Of Multi-temporal Remote Sensing Images

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Title: Analysis Of Multi-temporal Remote Sensing Images
Description: The development of effective methodologies for the analysis of multi-temporal data is one of the most important and challenging issues that the remote sensing community will face in the coming years. Its importance and timeliness are directly related to the ever-increasing quantity of multi-temporal data provided by the numerous remote sensing satellites that orbit our planet. The synergistic use of multi-temporal remote sensing data and advanced analysis methodologies results in the possibility of solving complex problems related to the monitoring of the Earth's surface and atmosphere at different scales. However, the advances in the methodologies for the analysis of multi-temporal data have been significantly under-illuminated with respect to other remote sensing data analysis topics. In addition, the link between the end-users'needs and the scientific community needs to be strengthened.This volume of proceedings contains 43 contributions from researchers representing academia, industry and governmental organizations. It is organized into three thematic sections: Image Analysis and Algorithms; Analysis of Synthetic Aperture Radar Data; Monitoring and Management of Resources.
Authors: Paul C Smits, Lorenzo Bruzzone
Resource Type: eBook.
Subjects: Remote sensing--Data processing--Congresses
Categories: TECHNOLOGY & ENGINEERING / Remote Sensing & Geographic Information Systems, COMPUTERS / Optical Data Processing, SCIENCE / Environmental Science
Database: eBook Collection (EBSCOhost)
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  Data: The development of effective methodologies for the analysis of multi-temporal data is one of the most important and challenging issues that the remote sensing community will face in the coming years. Its importance and timeliness are directly related to the ever-increasing quantity of multi-temporal data provided by the numerous remote sensing satellites that orbit our planet. The synergistic use of multi-temporal remote sensing data and advanced analysis methodologies results in the possibility of solving complex problems related to the monitoring of the Earth's surface and atmosphere at different scales. However, the advances in the methodologies for the analysis of multi-temporal data have been significantly under-illuminated with respect to other remote sensing data analysis topics. In addition, the link between the end-users'needs and the scientific community needs to be strengthened.This volume of proceedings contains 43 contributions from researchers representing academia, industry and governmental organizations. It is organized into three thematic sections: Image Analysis and Algorithms; Analysis of Synthetic Aperture Radar Data; Monitoring and Management of Resources.
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  Data: <searchLink fieldCode="AR" term="%22Paul+C+Smits%22">Paul C Smits</searchLink><br /><searchLink fieldCode="AR" term="%22Lorenzo+Bruzzone%22">Lorenzo Bruzzone</searchLink>
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RecordInfo BibRecord:
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      – Code: 621.3678
        Scheme: ddc
        Type: prePub
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Remote sensing--Data processing--Congresses
        Type: general
    Titles:
      – TitleFull: Analysis Of Multi-temporal Remote Sensing Images
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Paul C Smits
      – PersonEntity:
          Name:
            NameFull: Lorenzo Bruzzone
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          Name:
            NameFull: Paul C Smits
      – PersonEntity:
          Name:
            NameFull: Lorenzo Bruzzone
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          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2004
            – D: 04
              M: 02
              Type: profile
              Y: 2014
          Identifiers:
            – Type: isbn-print
              Value: 9789812389152
            – Type: isbn-electronic
              Value: 9789812702630
          Numbering:
            – Type: volume
              Value: 00003
          Titles:
            – TitleFull: Analysis Of Multi-temporal Remote Sensing Images
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