Research Papers

Pan-sharpening algorithm to remove thin cloud via mask dodging and nonsampled shift-invariant shearlet transform

[+] Author Affiliations
Cheng Shi

Xidian University, School of Computer Science and Technology, Xi’an 710071, China

Xidian University, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xi’an 710071, China

International Research Center for Intelligent Perception and Computation Xidian University, Shaanxi Province, Xi’an 710071, China

Fang Liu

Xidian University, School of Computer Science and Technology, Xi’an 710071, China

Xidian University, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xi’an 710071, China

International Research Center for Intelligent Perception and Computation Xidian University, Shaanxi Province, Xi’an 710071, China

Ling-Ling Li

Xidian University, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xi’an 710071, China

International Research Center for Intelligent Perception and Computation Xidian University, Shaanxi Province, Xi’an 710071, China

Hong-Xia Hao

Xidian University, School of Computer Science and Technology, Xi’an 710071, China

Xidian University, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xi’an 710071, China

International Research Center for Intelligent Perception and Computation Xidian University, Shaanxi Province, Xi’an 710071, China

J. Appl. Remote Sens. 8(1), 083658 (Mar 26, 2014). doi:10.1117/1.JRS.8.083658
History: Received October 17, 2013; Revised January 28, 2014; Accepted February 20, 2014
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Abstract.  The goal of pan-sharpening is to get an image with higher spatial resolution and better spectral information. However, the resolution of the pan-sharpened image is seriously affected by the thin clouds. For a single image, filtering algorithms are widely used to remove clouds. These kinds of methods can remove clouds effectively, but the detail lost in the cloud removal image is also serious. To solve this problem, a pan-sharpening algorithm to remove thin cloud via mask dodging and nonsampled shift-invariant shearlet transform (NSST) is proposed. For the low-resolution multispectral (LR MS) and high-resolution panchromatic images with thin clouds, a mask dodging method is used to remove clouds. For the cloud removal LR MS image, an adaptive principal component analysis transform is proposed to balance the spectral information and spatial resolution in the pan-sharpened image. Since the clouds removal process causes the detail loss problem, a weight matrix is designed to enhance the details of the cloud regions in the pan-sharpening process, but noncloud regions remain unchanged. And the details of the image are obtained by NSST. Experimental results over visible and evaluation metrics demonstrate that the proposed method can keep better spectral information and spatial resolution, especially for the images with thin clouds.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Cheng Shi ; Fang Liu ; Ling-Ling Li and Hong-Xia Hao
"Pan-sharpening algorithm to remove thin cloud via mask dodging and nonsampled shift-invariant shearlet transform", J. Appl. Remote Sens. 8(1), 083658 (Mar 26, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.083658


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