Show simple item record

dc.contributor.authorEsmaeili Salehani, Yaser
dc.contributor.otherQueen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.))en
dc.date2016-10-05 09:34:35.463en
dc.date.accessioned2016-10-05T16:20:47Z
dc.date.available2016-10-05T16:20:47Z
dc.date.issued2016-10-05
dc.identifier.urihttp://hdl.handle.net/1974/15072
dc.descriptionThesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2016-10-05 09:34:35.463en
dc.description.abstractSpectral unmixing (SU) is a technique to characterize mixed pixels of the hyperspectral images measured by remote sensors. Most of the existing spectral unmixing algorithms are developed using the linear mixing models. Since the number of endmembers/materials present at each mixed pixel is normally scanty compared with the number of total endmembers (the dimension of spectral library), the problem becomes sparse. This thesis introduces sparse hyperspectral unmixing methods for the linear mixing model through two different scenarios. In the first scenario, the library of spectral signatures is assumed to be known and the main problem is to find the minimum number of endmembers under a reasonable small approximation error. Mathematically, the corresponding problem is called the $\ell_0$-norm problem which is NP-hard problem. Our main study for the first part of thesis is to find more accurate and reliable approximations of $\ell_0$-norm term and propose sparse unmixing methods via such approximations. The resulting methods are shown considerable improvements to reconstruct the fractional abundances of endmembers in comparison with state-of-the-art methods such as having lower reconstruction errors. In the second part of the thesis, the first scenario (i.e., dictionary-aided semiblind unmixing scheme) will be generalized as the blind unmixing scenario that the library of spectral signatures is also estimated. We apply the nonnegative matrix factorization (NMF) method for proposing new unmixing methods due to its noticeable supports such as considering the nonnegativity constraints of two decomposed matrices. Furthermore, we introduce new cost functions through some statistical and physical features of spectral signatures of materials (SSoM) and hyperspectral pixels such as the collaborative property of hyperspectral pixels and the mathematical representation of the concentrated energy of SSoM for the first few subbands. Finally, we introduce sparse unmixing methods for the blind scenario and evaluate the efficiency of the proposed methods via simulations over synthetic and real hyperspectral data sets. The results illustrate considerable enhancements to estimate the spectral library of materials and their fractional abundances such as smaller values of spectral angle distance (SAD) and abundance angle distance (AAD) as well.en_US
dc.languageenen
dc.language.isoenen_US
dc.relation.ispartofseriesCanadian thesesen
dc.rightsQueen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canadaen
dc.rightsProQuest PhD and Master's Theses International Dissemination Agreementen
dc.rightsIntellectual Property Guidelines at Queen's Universityen
dc.rightsCopying and Preserving Your Thesisen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjectNon-negative matrix factorizationen_US
dc.subjectlinear mixing modelen_US
dc.subjectsparse spectral unmixingen_US
dc.subjectLp-normen_US
dc.subjectHyperspectral imagingen_US
dc.subjectArctan functionen_US
dc.subjectL0-normen_US
dc.titleLinear Hyperspectral Unmixing Using L0-norm Approximations and Nonnegative Matrix Factorizationen_US
dc.typethesisen_US
dc.description.degreePh.Den
dc.contributor.supervisorGazor, Saeeden
dc.contributor.supervisorYousefi, Shahramen
dc.contributor.supervisorKim, Il-Minen
dc.contributor.departmentElectrical and Computer Engineeringen


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record