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Compressive Sensing for Pan Sharpening

Wang, Xuan (2011) Compressive Sensing for Pan Sharpening. Master's, Technische Universität München (TUM).

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Abstract

In the mid 1980s, pan-sharpening caught significant attention in remote sensing and image processing, as SPOT 1 (launched in 1986) provided high resolution (HR) pan images and corresponding low resolution (LR) multispectral images. Since that time, intensive research has been carried out to develop efficient methods for generating fused images with both high spatial and spectral resolution. Compressive sensing (CS) is a state-of-the-art signal processing technique. Its super-resolution capability and robustness have been demonstrated. This article presents a new sophisticated pan-sharpening method based on CS. Visual and statistic analysis show superior performance of the proposed method compared to the existing conventional pan-sharpening methods in general, i.e. rich in spatial information and less spectral distortion. Moreover, popular quality assessment metrics are employed to explore the dependency on regularization parameters and to compare the efficiency of various sparse reconstruction toolboxes. In addition, for a more general application, i.e. no HR pan image is available, the proposed algorithm is extended by introducing a beforehand co-trained global over-complete dictionary pair.

Document Type:Thesis (Master's)
Title:Compressive Sensing for Pan Sharpening
Authors:
AuthorsInstitution or Email of Authors
Wang, Xuanfayewx0519@gmail.com
Date:February 2011
Keywords:compressive sensing, pan-sharpening, dictionary learning, sparse coefficients estimation
Institution:Technische Universität München (TUM)
Department:Lehrstuhl für Methodik der Fernerkundung
HGF - Research field:Aeronautics, Space and Transport (old)
HGF - Program:Space (old)
HGF - Program Themes:W - no assignement
DLR - Research area:Space
DLR - Program:W - no assignement
DLR - Research theme (Project):W -- no assignement (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > SAR Signal Processing
Deposited By: Xiao Xiang Zhu
Deposited On:30 Mar 2011 15:54
Last Modified:30 Jan 2012 13:21

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