• Login
    View Item 
    •   Home
    • Research
    • Conference Papers
    • View Item
    •   Home
    • Research
    • Conference Papers
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguideTheses and Dissertations LibguideSubmit an Item

    Statistics

    Display statistics

    Monitoring land-cover changes by combining a detection step with a classification step

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    SSCI-2018-LCCD.pdf
    Size:
    756.3Kb
    Format:
    PDF
    Description:
    Accepted Manuscript
    Download
    Type
    Conference Paper
    Authors
    Harrou, Fouzi cc
    Zerrouki, Nabil
    Sun, Ying cc
    Hocini, Lotfi
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Environmental Statistics Group
    Statistics Program
    KAUST Grant Number
    OSR-2015-CRG4-2582
    Date
    2019-02-28
    Online Publication Date
    2019-02-28
    Print Publication Date
    2018-11
    Permanent link to this record
    http://hdl.handle.net/10754/631693
    
    Metadata
    Show full item record
    Abstract
    An approach merging the HotellingT 2 control scheme with weighted random forest classifier is proposed and used in the context of detecting land cover changes via remote sensing and radiometric measurements. HotellingT 2 procedure is introduced to identify features corresponding to changed areas. However, T 2 scheme is not able to separate real from false changes. To tackle this limitation, the weighted random forest algorithm, which is an efficient classification technique for unbalanced problems, has been successfully applied on features of the detected pixels to recognize the type of change. The performance of the algorithm is evaluated using SZTAKI AirChange benchmark data, results show that the proposed detection scheme succeeds to appropriately identify changes to land cover. Also, we compared the proposed approach to that of the conventional algorithms (i.e., neural network, random forest, support vector machine and k-nearest neighbors) and found improved performance.
    Citation
    Harrou F, Zerrouki N, Sun Y, Hocini L (2018) Monitoring land-cover changes by combining a detection step with a classification step. 2018 IEEE Symposium Series on Computational Intelligence (SSCI). Available: http://dx.doi.org/10.1109/SSCI.2018.8628774.
    Sponsors
    This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582. The authors (Nabil Zerrouki and Lotfi H Hocini) would like to thank the DIIM laboratory, Centre de Developpement des Technologies Avancees (CDTA) for the continued support during the research.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2018 IEEE Symposium Series on Computational Intelligence (SSCI)
    Conference/Event name
    8th IEEE Symposium Series on Computational Intelligence, SSCI 2018
    DOI
    10.1109/SSCI.2018.8628774
    Additional Links
    https://ieeexplore.ieee.org/document/8628774
    ae974a485f413a2113503eed53cd6c53
    10.1109/SSCI.2018.8628774
    Scopus Count
    Collections
    Conference Papers; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

    entitlement

     
    DSpace software copyright © 2002-2022  DuraSpace
    Quick Guide | Contact Us | KAUST University Library
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.