NDLI: An Online Coupled Dictionary Learning Approach for Remote Sensing Image Fusion

Content Provider	IEEE Xplore Digital Library
Author	Min Guo Hongyan Zhang Jiayi Li Liangpei Zhang Huanfeng Shen
Copyright Year	2008
Abstract	Most earth observation satellites, such as IKONOS, QuickBird, GeoEye, and WorldView-2, provide a high spatial resolution (HR) panchromatic (Pan) image and a multispectral (MS) image at a lower spatial resolution (LR). Image fusion is an effective way to acquire the HR MS images that are widely used in various applications. In this paper, we propose an online coupled dictionary learning (OCDL) approach for image fusion, in which a superposition strategy is applied to construct the coupled dictionaries. The constructed coupled dictionaries are further developed via an iterative update to ensure that the HR MS image patch can be almost identically reconstructed by multiplying the HR dictionary and the sparse coefficient vector, which is solved by sparsely representing its counterpart LR MS image patch over the LR dictionary. The fusion results from IKONOS and WorldView-2 data show that the proposed fusion method is competitive or even superior to the other state-of-the-art fusion methods.
Starting Page	1284
Ending Page	1294
Page Count	11
File Size	2538609
File Format	PDF
ISSN	19391404
Volume Number	7
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dictionaries Image fusion Vectors Remote sensing Image reconstruction Spatial resolution sparse representation (SR) Coupled dictionary image fusion remote sensing imagery
Content Type	Text
Resource Type	Article
Subject	Atmospheric Science Computers in Earth Sciences

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