Full metadata record
DC FieldValueLanguage
dc.contributorDepartment of Land Surveying and Geo-Informaticsen_US
dc.contributor.advisorShi, Wenzhong (LSGI)-
dc.creatorWang, Qunming-
dc.publisherHong Kong Polytechnic University-
dc.rightsAll rights reserveden_US
dc.titleSoft-then-hard sub-pixel mapping algorithm for remote sensing imagesen_US
dcterms.abstractImage classification, one of the most important techniques in remote sensing, is used widely to extract land cover information from remote sensing images. The inevitable mixed pixels in remote sensing images have brought a great challenge for traditional hard classification-based land cover mapping. To solve this mixed pixel problem, soft classification (e.g., spectral unmixing) has been developed to predict land cover proportions for land cover classes that have a spatial frequency higher than the interval between pixels. Soft classifiers exploit the spectral information of remote sensing images, but fail to predict the spatial location of classes within mixed pixels. To address this issue, sub-pixel mapping (SPM) has been developed, in which each mixed pixel is divided into multiple sub-pixels for which class labels are predicted. SPM, thus, transforms a soft classification into a finer resolution hard classification. SPM is also termed super-resolution mapping in remote sensing. It has been receiving increasing attention in recent years. In this thesis, the soft-then-hard SPM (STHSPM) algorithms are summarized for the first time. STHSPM is a type of SPM algorithm consisting of soft class value (between 0 and 1) estimation at fine spatial resolution and hard class allocation for sub-pixels. The STHSPM algorithms provide a good opportunity to achieve SPM solutions quickly. Furthermore, they provide important insight into SPM and open doors to more alternatives. This thesis focuses on the STHSPM algorithm and the main research includes developing new class allocation approaches for the STHSPM algorithms, using additional information in STHSPM to enhance SPM, developing new STHSPM algorithms and applying STHSPM in sub-pixel resolution change detection. Specifically, a new class allocation approach that allocates classes in units of class (UOC) is proposed and UOC is further extended with an adaptive scheme, called AUOC; The multiple shifted images are incorporated to the STHSPM algorithms to decrease the uncertainty in SPM; Two new STHSPM algorithms, radial basis function interpolation and naive indicator cokriging, are proposed; STHSPM is proposed for fast sub-pixel resolution change detection. The experimental results demonstrate the feasibilities of the proposed methods in this thesis.en_US
dcterms.extent189 pages : illustrations (some color)en_US
dcterms.isPartOfPolyU Electronic Thesesen_US
dc.description.awardFCE Awards for Outstanding PhD Theses-
dcterms.educationalLevelAll Doctorateen_US
dcterms.LCSHRemote sensing -- Mathematical models.en_US
dcterms.LCSHImage analysis -- Mathematical models.en_US
dcterms.LCSHHong Kong Polytechnic University -- Dissertationsen_US
dcterms.accessRightsopen accessen_US

Files in This Item:
File Description SizeFormat 
b28237456.pdfFor All Users12.33 MBAdobe PDFView/Open

Copyright Undertaking

As a bona fide Library user, I declare that:

  1. I will abide by the rules and legal ordinances governing copyright regarding the use of the Database.
  2. I will use the Database for the purpose of my research or private study only and not for circulation or further reproduction or any other purpose.
  3. I agree to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage.

By downloading any item(s) listed above, you acknowledge that you have read and understood the copyright undertaking as stated above, and agree to be bound by all of its terms.

Show simple item record

Please use this identifier to cite or link to this item: https://theses.lib.polyu.edu.hk/handle/200/8101