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Sparse Methods for Hyperspectral Unmixing and Image Fusion

Bieniarz, Jakub (2015) Sparse Methods for Hyperspectral Unmixing and Image Fusion. Dissertation, University of Osnabrück.

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In recent years, the substantial increase in the number of spectral channels in optical remote sensing sensors allows more detailed spectroscopic analysis of objects on the Earth surface. Modern hyperspectral sensors are able to sample the sunlight reflected from a target on the ground with hundreds of adjacent narrow spectral channels. However, the increased spectral resolution comes at the price of a lower spatial resolution, e.g. the forthcoming German hyperspectral sensor Environmental Mapping and Analysis Program (EnMAP) which will have 244 spectral channels and a pixel size on ground as large as 30 m $\times$ 30 m. The main aim of this thesis is dealing with the problem of reduced spatial resolution in hyperspectral sensors. This is addressed first as an unmixing problem, i.e., extraction and quantification of the spectra of pure materials mixed in a single pixel, and second as a resolution enhancement problem based on fusion of multispectral and hyperspectral imagery. This thesis proposes novel methods for hyperspectral unmixing using sparse approximation techniques and external spectral dictionaries, which unlike traditional least squares-based methods, do not require pure material spectrum selection step and are thus able to simultaneously estimate the underlying active materials along with their respective abundances. However, in previous works it has been shown that these methods suffer from some drawbacks, mainly from the intra dictionary coherence. To improve the performance of sparse spectral unmixing, the use of derivative transformation and a novel two step group unmixing algorithm are proposed. Additionally, the spatial homogeneity of abundance vectors by introducing a multi-look model for spectral unmixing is exploited.% Based on the above findings, a new method for fusion of hyperspectral images with higher spatial resolution multispectral images is proposed. The algorithm exploits the spectral information of the hyperspectral image and the spatial information from the multispectral image by means of sparse spectral unmixing to form a new high spatial and spectral resolution hyperspectral image. The introduced method is robust when applied to highly mixed scenarios as it relies on external spectral dictionaries. Both the proposed sparse spectral unmixing algorithms as well as the resolution enhancement approach are evaluated quantitatively and qualitatively. Algorithms developed in this thesis are significantly faster and yield better or similar results to state-of-the-art methods.

Item URL in elib:https://elib.dlr.de/103910/
Document Type:Thesis (Dissertation)
Title:Sparse Methods for Hyperspectral Unmixing and Image Fusion
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Bieniarz, Jakubjakub.bieniarz (at) dlr.deUNSPECIFIED
Date:2 December 2015
Journal or Publication Title:Sparse Methods for Hyperspectral Unmixing and Image Fusion
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
Number of Pages:168
Keywords:Hyperspectral Unmixing, Image Fusion, Sparse approximation
Institution:University of Osnabrück
Department:Fachbereich Mathematik/Informatik
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Bieniarz, Jakub
Deposited On:14 Apr 2016 11:01
Last Modified:31 Jul 2019 20:01

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