A GIHS-based spectral preservation fusion method for remote sensing images using edge restored spectral modulation

Xiran Zhou, Jun Liu*, Shuguang Liu, Lei Cao, Qiming ZHOU, Huawen Huang

*Corresponding author for this work

    Research output: Contribution to journalJournal articlepeer-review

    71 Citations (Scopus)

    Abstract

    High spatial resolution and spectral fidelity are basic standards for evaluating an image fusion algorithm. Numerous fusion methods for remote sensing images have been developed. Some of these methods are based on the intensity-hue-saturation (IHS) transform and the generalized IHS (GIHS), which may cause serious spectral distortion. Spectral distortion in the GIHS is proven to result from changes in saturation during fusion. Therefore, reducing such changes can achieve high spectral. fidelity. A GIHS-based spectral preservation fusion method that can. theoretically reduce spectral. distortion is proposed in this study. The proposed algorithm consists of two steps. The first step is spectral modulation (SM), which uses the Gaussian function to extract spatial details and conduct SM of multispectral (MS) images. This method yields a desirable visual effect without requiring histogram matching between the panchromatic image and the intensity of the MS image. The second step uses the Gaussian convolution function to restore lost edge details during SM. The proposed method is proven effective and shown to provide better results compared with other GIHS-based methods.

    Original languageEnglish
    Pages (from-to)16-27
    Number of pages12
    JournalISPRS Journal of Photogrammetry and Remote Sensing
    Volume88
    DOIs
    Publication statusPublished - Feb 2014

    Scopus Subject Areas

    • Atomic and Molecular Physics, and Optics
    • Engineering (miscellaneous)
    • Computer Science Applications
    • Computers in Earth Sciences

    User-Defined Keywords

    • Edge restored spectral modulation
    • Generalized intensity-hue-saturation
    • Image fusion
    • Image quality evaluation
    • Remote sensing
    • Spectral distortion

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