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Journal of Applied Remote Sensing

Guided filter and principal component analysis hybrid method for hyperspectral pansharpening
Author(s): Jiahui Qu; Yunsong Li; Wenqian Dong
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Paper Abstract

Hyperspectral (HS) pansharpening aims to generate a fused HS image with high spectral and spatial resolution through integrating an HS image with a panchromatic (PAN) image. A guided filter (GF) and principal component analysis (PCA) hybrid HS pansharpening method is proposed. First, the HS image is interpolated and the PCA transformation is performed on the interpolated HS image. The first principal component ( PC 1 ) channel concentrates on the spatial information of the HS image. Different from the traditional PCA method, the proposed method sharpens the PAN image and utilizes the GF to obtain the spatial information difference between the HS image and the enhanced PAN image. Then, in order to reduce spectral and spatial distortion, an appropriate tradeoff parameter is defined and the spatial information difference is injected into the PC 1 channel through multiplying by this tradeoff parameter. Once the new PC 1 channel is obtained, the fused image is finally generated by the inverse PCA transformation. Experiments performed on both synthetic and real datasets show that the proposed method outperforms other several state-of-the-art HS pansharpening methods in both subjective and objective evaluations.

Paper Details

Date Published: 11 January 2018
PDF: 18 pages
J. Appl. Rem. Sens. 12(1) 015003 doi: 10.1117/1.JRS.12.015003
Published in: Journal of Applied Remote Sensing Volume 12, Issue 1
Show Author Affiliations
Jiahui Qu, Xidian Univ. (China)
Yunsong Li, Xidian Univ. (China)
Wenqian Dong, Xidian Univ. (China)


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