11 January 2018 Guided filter and principal component analysis hybrid method for hyperspectral pansharpening
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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.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Jiahui Qu, Jiahui Qu, Yunsong Li, Yunsong Li, Wenqian Dong, 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 . Submission: Received: 5 May 2017; Accepted: 12 December 2017
Received: 5 May 2017; Accepted: 12 December 2017; Published: 11 January 2018
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