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A Novel Pan-Sharpening Framework Based on Matting Model and Multiscale Transform

Yong Yang,Weiguo Wan,Shuying Huang,P. Lin,Yue Que

Published 2017 in Remote Sensing

ABSTRACT

Pan-sharpening aims to sharpen a low spatial resolution multispectral (MS) image by combining the spatial detail information extracted from a panchromatic (PAN) image. An effective pan-sharpening method should produce a high spatial resolution MS image while preserving more spectral information. Unlike traditional intensity-hue-saturation (IHS)- and principal component analysis (PCA)-based multiscale transform methods, a novel pan-sharpening framework based on the matting model (MM) and multiscale transform is presented in this paper. First, we use the intensity component (I) of the MS image as the alpha channel to generate the spectral foreground and background. Then, an appropriate multiscale transform is utilized to fuse the PAN image and the upsampled I component to obtain the fused high-resolution gray image. In the fusion, two preeminent fusion rules are proposed to fuse the low- and high-frequency coefficients in the transform domain. Finally, the high-resolution sharpened MS image is obtained by linearly compositing the fused gray image with the upsampled foreground and background images. The proposed framework is the first work in the pan-sharpening field. A large number of experiments were tested on various satellite datasets; the subjective visual and objective evaluation results indicate that the proposed method performs better than the IHS- and PCA-based frameworks, as well as other state-of-the-art pan-sharpening methods both in terms of spatial quality and spectral maintenance.

PUBLICATION RECORD

  • Publication year

    2017

  • Venue

    Remote Sensing

  • Publication date

    2017-04-21

  • Fields of study

    Computer Science, Engineering, Environmental Science

  • Identifiers
    DOI 10.3390/rs9040391
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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