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Pan-sharpening via regional division and NSST

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Abstract

In this paper, a novel Pan-sharpening algorithm for high resolution Panchromatic (HR PAN) and low resolution multispectral image (LR MS) via regional division and Non-sampled shift-invariance shearlet transform (NSST) is proposed. The purpose of our algorithm is to fuse the LR MS and the HR PAN image for different objects respectively, in order to solve the spectral distortion and spatial resolution problems in the Pan-sharpened image. Firstly, the LR MS and the HR PAN images are divided into structure and non-structure regions respectively, and a regional association map is set according to the division result. A regional similarity measure, degree of regional match (DRM), is proposed to evaluate the correction of the two regions. And a fusion rule is designed based on DRM. Because of the flexibility direction features, NSST can represent the edge information of the image better. Hence the LR MS and the HR PAN images are decomposed by the NSST, and the Pan-sharpened image can be obtained by the designed rule. Experimental results have proved that the proposed algorithm has a better Pan-sharpening result than other methods do.

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Acknowledgments

The work was jointly supported by the National Basic Research Program (973 Programs) of China (No. 2013CB329402), the National Natural Science Foundations of China (No. 61173090, 61072109, 61272280, 41271447, 61272195), the Program for New Century Excellent Talents in Universtity (NCET-12-0919), The Fundamental Research Funds for the Central Universities (No. K5051203020, K5051203001, K5051303016, K5051303018 and K50513100006), the Creative Project of Science and Technology State of xi’an (No. CXY1341(6)), the National Research Foundation for the Doctoral Program of Higher Education of China (No. 20110203110006), the Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) (No. B07048), the Program for Cheung Kong Scholars and Innovative Research Team University (NO. IRT1170). An EU FP7 IRSES grant (No. 247619) on “Nature Inspired Computation and its Applications (NICaiA)”.

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Authors and Affiliations

  1. School of Computer Science and Technology, Xidian University, Xi’an, Shaanxi, People’s Republic of China

    Cheng Shi, Fang Liu & Qiguang Miao

  2. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xi’an, Shaanxi, People’s Republic of China

    Cheng Shi & Fang Liu

  3. International Research Center for Intelligent Perception and Computation, Xidian University, Xi’an, Shaanxi, 710071, People’s Republic of China

    Cheng Shi & Fang Liu

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  1. Cheng Shi
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  2. Fang Liu
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Correspondence to Qiguang Miao.

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Shi, C., Liu, F. & Miao, Q. Pan-sharpening via regional division and NSST. Multimed Tools Appl 74, 7843–7857 (2015). https://doi.org/10.1007/s11042-014-2027-x

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  • DOI: https://doi.org/10.1007/s11042-014-2027-x

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