dc.contributor.author Esmaeili Salehani, Yaser dc.contributor.other Queen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.)) en dc.date 2016-10-05 09:34:35.463 en dc.date.accessioned 2016-10-05T16:20:47Z dc.date.available 2016-10-05T16:20:47Z dc.date.issued 2016-10-05 dc.identifier.uri http://hdl.handle.net/1974/15072 dc.description Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2016-10-05 09:34:35.463 en dc.description.abstract Spectral 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.language en en dc.language.iso en en_US dc.relation.ispartofseries Canadian theses en dc.rights Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada en dc.rights ProQuest PhD and Master's Theses International Dissemination Agreement en dc.rights Intellectual Property Guidelines at Queen's University en dc.rights Copying and Preserving Your Thesis en dc.rights This 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.subject Non-negative matrix factorization en_US dc.subject linear mixing model en_US dc.subject sparse spectral unmixing en_US dc.subject Lp-norm en_US dc.subject Hyperspectral imaging en_US dc.subject Arctan function en_US dc.subject L0-norm en_US dc.title Linear Hyperspectral Unmixing Using L0-norm Approximations and Nonnegative Matrix Factorization en_US dc.type thesis en_US dc.description.degree Ph.D en dc.contributor.supervisor Gazor, Saeed en dc.contributor.supervisor Yousefi, Shahram en dc.contributor.supervisor Kim, Il-Min en dc.contributor.department Electrical and Computer Engineering en
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