NDLI: Locally linear embedding based example learning for pan-sharpening

Content Provider	IEEE Xplore Digital Library
Author	Qingjie Liu Lining Liu Yunhong Wang Zhaoxiang Zhang
Copyright Year	2012
Description	Author affiliation: State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China (Qingjie Liu; Lining Liu; Yunhong Wang; Zhaoxiang Zhang)
Abstract	In this paper, a novel example based method is proposed to solve the remote sensing pan-sharpening problem, utilizing an implicit non-parametric learning framework. The high resolution (HR) and downsam-pled panchromatic (PAN) images are used to train the high/low resolution patch pair dictionaries. Based on the perspective of locally linear embedding (LLE), every patch in each multi-spectral (MS) image band is modeled by its K nearest neighbors in patch set generated from low resolution (LR) PAN image, and this model can be generalized to the HR condition. The intended HR MS patch is reconstructed from the corresponding neighbors in HR PAN patches. Finally, the HR MS images are recovered by stitching these patches together. Two datasets of images acquired by Quick-Bird satellite are used to test the performance of the proposed method. Experimental results show that the proposed method performs well in preserving spectral information as well as spatial details.
Starting Page	1928
Ending Page	1931
File Size	688421
Page Count	4
File Format	PDF
ISBN	9781467322164
ISSN	10514651
e-ISBN	9784990644109
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-11
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	ICPR Org Committee
Subject Keyword	Spatial resolution Image reconstruction Principal component analysis Training Remote sensing Vectors
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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