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Practical remote sensing image fusion method based on guided filter and improved SML in the NSST domain

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Abstract

Due to the different characteristics of image modality, the panchromatic (PAN) and multispectral (MS) images include complementary and redundancy information in the spatial and spectral resolutions. Image fusion is an effective way to integrate the source PAN and MS images to obtain high-resolution MS image. In this paper, a novel remote sensing image fusion scheme in non-subsample Shearlet transform (NSST) domain is presented. An enhancement strategy is designed to solve the insufficiency of spatial detail in multiresolution analysis (MRA)-based methods after the intensity–hue–saturation (IHS) color space transform. Then, in the NSST fusion process, a guided filter-based low-frequency coefficient fusion rule and an improved sum-modified-Laplacian (SML)-based high-frequency coefficient fusion rule are proposed. The final fused image can be obtained through the inverse NSST transform and inverse IHS transform. Two different groups of satellite dataset are utilized to evaluate the fusion performance. The experiment results demonstrate that the proposed approach can achieve more spatial details and less spectral distortion compared with the existing methods regarding both the visual quality and the objective measurements.

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Acknowledgements

This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-R0992-15-1023) supervised by the IITP (Institute for Information & communications Technology Promotion). This research was also supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (GR 2016R1D1A3B03931911). And this study was also financially supported by the Grants of China Scholarship Council (CSC No.2017 08260057).

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Authors and Affiliations

  1. Division of Computer Science and Engineering, Chonbuk National University, Jeonju, Korea

    Weiguo Wan & Hyo Jong Lee

  2. School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, China

    Yong Yang

  3. Center for Advanced Image and Information Technology, Chonbuk National University, Jeonju, Korea

    Hyo Jong Lee

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  1. Weiguo Wan
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  2. Yong Yang
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  3. Hyo Jong Lee
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Correspondence to Hyo Jong Lee.

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Wan, W., Yang, Y. & Lee, H.J. Practical remote sensing image fusion method based on guided filter and improved SML in the NSST domain. SIViP 12, 959–966 (2018). https://doi.org/10.1007/s11760-018-1240-x

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