Guided filter and principal component analysis hybrid method for hyperspectral pansharpening

Jiahui Qu; Yunsong Li; Wenqian Dong

doi:10.1117/1.JRS.12.015003

11 January 2018 Guided filter and principal component analysis hybrid method for hyperspectral pansharpening

Jiahui Qu, Yunsong Li, Wenqian Dong

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 12, Issue 1, 015003 (January 2018). https://doi.org/10.1117/1.JRS.12.015003

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 ( PC1) 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 PC1 channel through multiplying by this tradeoff parameter. Once the new PC1 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.

Citation Download Citation

Jiahui Qu, Yunsong Li, and Wenqian Dong "Guided filter and principal component analysis hybrid method for hyperspectral pansharpening," Journal of Applied Remote Sensing 12(1), 015003 (11 January 2018). https://doi.org/10.1117/1.JRS.12.015003

Received: 5 May 2017; Accepted: 12 December 2017; Published: 11 January 2018

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