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Efficient sparse signal recovery of remote sensing data: a classification method for hyperspectral image data

Abdipourchenarestansofla, Morteza (2019) Efficient sparse signal recovery of remote sensing data: a classification method for hyperspectral image data. Master's, Hochschule Neubrandenburg.

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Abstract

Nowadays the concern of finding an efficient algorithm that can answer some of the open questions in big data analysis and mining has been gradually arose. Such questions can be regarded by the question of representing the data in a meaningful way in which the most useful information highlighted. Therefore, the motivation of answering these questions encourage this thesis to develop a principle classification algorithm called Efficient sparse signal recovery for big data representation for a classification task. In this thesis, we develop a classification principle algorithm that is based on the sparse coding for the classification of given test pixel from a hyperspectral image. Hyperspectral imagery in remote sensing domain has the characteristic of big data in terms of velocity, verity and volume. This data is a set of non-homogenous system that expose the ill-posed problem. Thus, a robust and efficient algorithm must be developed to treat such data effectively. Sparse representation draws a great attention in hyperspectral image representation and analysis. Employing sparsity-based model involved two main problems. Firstly, the problem of the representation of an informative dictionary, and secondly the issue of implementing a proper optimization problem that can effectively solve the objective function. This thesis focuses on the latter aspect while the dictionary issue is also tackled by proposing a Geometric dictionary. There have been many algorithms for finding the optimized minimum of the well-known objective functionals “least square ” with

Item URL in elib:https://elib.dlr.de/131849/
Document Type:Thesis (Master's)
Title:Efficient sparse signal recovery of remote sensing data: a classification method for hyperspectral image data
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Abdipourchenarestansofla, MortezaHochschule NeubrandenburgUNSPECIFIED
Date:2019
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Number of Pages:145
Status:Published
Keywords:Remote sensing, Hyperspectral data, Sparse signal recovery, Classification
Institution:Hochschule Neubrandenburg
Department:Fachbereich Landschaftswissenschaften und Geomatik
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 - Remote sensing and geoscience
Location: Neustrelitz
Institutes and Institutions:German Remote Sensing Data Center > National Ground Segment
Deposited By: Borg, Dr.rer.nat. Erik
Deposited On:02 Dec 2019 11:13
Last Modified:18 Dec 2019 10:42

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