|Title:||Soft-then-hard sub-pixel mapping algorithm for remote sensing images|
|Advisors:||Shi, Wenzhong (LSGI)|
|Subject:||Remote sensing -- Mathematical models.|
Image analysis -- Mathematical models.
Hong Kong Polytechnic University -- Dissertations
|Department:||Department of Land Surveying and Geo-Informatics|
|Pages:||189 pages : illustrations (some color)|
|Abstract:||Image 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.|
|Rights:||All rights reserved|
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