Pan-sharpening based on geometric clustered neighbor embedding

Qingjie Liu; Yunhong Wang; Zhaoxiang Zhang

doi:10.1117/1.OE.53.9.093109

26 September 2014 Pan-sharpening based on geometric clustered neighbor embedding

Qingjie Liu, Yunhong Wang, Zhaoxiang Zhang

Author Affiliations +

Optical Engineering, Vol. 53, Issue 9, 093109 (September 2014). https://doi.org/10.1117/1.OE.53.9.093109

Abstract

Utilizing an implicit nonparametric learning framework, a neighbor-embedding-based method is proposed to solve the remote-sensing pan-sharpening problem. First, the original high-resolution (HR) and downsampled panchromatic (Pan) images are used to train the high/low-resolution (LR) patch pair dictionaries. Based on the perspective of locally linear embedding, patches in LR and HR images form manifolds with similar local intrinsic structure in the corresponding feature space. Every patch in each multispectral (MS) image band is modeled by its K nearest neighbors in the patch set generated from the LR Pan image, and this model can be generalized to the HR condition. Then, the desired HR MS patch is reconstructed from the corresponding neighbors in the HR Pan patch set. Finally, HR MS images are recovered by stitching these patches together. Recognizing that the K nearest neighbors should have local geometric structures similar to the input query patch based on clustering, we employ a dominant orientation algorithm to perform such clustering. The K nearest neighbors of each input LR MS patch are adaptively chosen from the associate subdictionary. Four datasets of images acquired by QuickBird and IKONOS satellites 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.

Citation Download Citation

Qingjie Liu, Yunhong Wang, and Zhaoxiang Zhang "Pan-sharpening based on geometric clustered neighbor embedding," Optical Engineering 53(9), 093109 (26 September 2014). https://doi.org/10.1117/1.OE.53.9.093109

Published: 26 September 2014

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available