Pan-sharpening using a guided filter

ABSTRACT

Pan-sharpening aims to integrate the spatial details of a high-resolution panchromatic (Pan) image with the spectral information of low-resolution multispectral (MS) images to produce high-resolution MS images. The key is to appropriately estimate the missing spatial details of the MS images while preserving their spectral contents. However, many existing methods extract the spatial details from the Pan image without fully considering the structures of the MS images, resulting in spectral distortion due to redundant detail injection. A guided filter can transfer the structures of the MS images into the intensity component or the low-pass approximation of the Pan image. Using the guided filter, we propose two novel pan-sharpening methods to reduce the redundant details among the MS and Pan images. Specifically, we extract the missing spatial details of the MS images by minimizing the difference between the Pan image and its corresponding filtering output, with the help of the MS images. Two different ways of using the MS images as guided images lead to two proposed methods, which can be grouped into component substitution (CS) family. Extensive experimental results over three data sets collected by different satellite sensors demonstrate the effectiveness of the proposed methods.

Acknowledgements

We would like to express our sincere gratitude to Dr Luciano Alparone and Gemine Vivone for sharing their pan-sharpening toolbox, to Dr Wenzhi Liao for the invaluable discussions with us, and to the Associate Editor (Prof. Timothy Warner) and two referees for their valuable comments and suggestions, which greatly improved the presentation of this paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported in part by the National Basic Research Programme of China [grant number 2013CB329404]; in part by the Key Project of Natural Science Foundation of China under [grant number 11131006], [grant number 91330204], in part by the Natural Science Foundation of China [grant number 11401465], [grant number 91230101], [grant number 61572393]; in part by the Fundamental Research Funds for the Central Universities [grant number xjj20140101]; in part by the Projects funded by China Postdoctoral Science Foundation [grant number 2014M560781]; and Shaanxi Postdoctoral Science Foundation.