Research Papers

New intensity-hue-saturation pan-sharpening method based on texture analysis and genetic algorithm-adaption

[+] Author Affiliations
Rasoul Masoudi

Iran University of Science and Technology, School of Computer Engineering, University Road, Hengam Street, Resalat Square, Tehran 16846-13114, Iran

Peyman Kabiri

Iran University of Science and Technology, School of Computer Engineering, University Road, Hengam Street, Resalat Square, Tehran 16846-13114, Iran

J. Appl. Remote Sens. 8(1), 083640 (May 01, 2014). doi:10.1117/1.JRS.8.083640
History: Received December 13, 2013; Revised March 1, 2014; Accepted March 17, 2014
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Abstract.  Pansharpening aims to fuse a low-resolution multispectral image with a high-resolution panchromatic image to create a multispectral image with high spatial and spectral resolution. The intensity-hue-saturation (IHS) fusion method transforms an image from RGB space to IHS space. This paper reports a method to improve the spectral resolution of a final multispectral image. The proposed method implies two modifications on the basic IHS method to improve the sharpness of the final image. First, the paper proposes a method based on a genetic algorithm to find the weight of each band of multispectral image in the fusion process. Later on, a texture-based technique is proposed to save the spectral information of the final image with respect to the texture boundaries. Spectral quality metrics in terms of SAM, SID, Q-average, RASE, RMSE, CC, ERGAS and UIQI are used in our experiments. Experimental results on IKONOS and QuickBird data show that the proposed method is more efficient than the original IHS-based fusion approach and some of its extensions, such as IKONOS IHS, edge-adaptive IHS and explicit band coefficient IHS, in preserving spectral information of multispectral images.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Rasoul Masoudi and Peyman Kabiri
"New intensity-hue-saturation pan-sharpening method based on texture analysis and genetic algorithm-adaption", J. Appl. Remote Sens. 8(1), 083640 (May 01, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.083640


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