Pan-sharpening high spatial resolution ratio images using optimization

Fangjun Li; Fu Chen; Jianbo Liu

doi:10.1117/12.832362

30 October 2009 Pan-sharpening high spatial resolution ratio images using optimization

Fangjun Li, Fu Chen, Jianbo Liu

Proceedings Volume 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications; 74980C (2009) https://doi.org/10.1117/12.832362
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

Among most of current Pan-sharpening methods, resampling is generally required to make panchromatic (Pan) and multispectral (MS) images matched correctly pixel by pixel. However, few methods have focused on spectral distortions caused by shape distortions of real features during resampling. This paper proposes a new Pan-sharpening algorithm based on the gray and spectral relationships between Pan, MS and the fused images. In the algorithm, Pan-sharpening is defined as an optimization of a linear overdetermined system. It takes Pan and original MS images as input datasets without resampling. The Least square technique is applied to calculate the optimum values (quality fused images). QuickBird image datasets are tested, and the results are compared with the fused images of IHS, PCA and Gram-Schmidt using interpolated MS image. The result shows that the proposed method is more efficient than IHS, PCA and Gram-Schmidt in preserving spectral characteristics and increasing spatial resolution, especially for high spatial resolution ratio (SRR > 4:1, spatial resolution ratio is the ratio of the spatial resolution of MS image to that of Pan image.) images.

Citation Download Citation

Fangjun Li, Fu Chen, and Jianbo Liu "Pan-sharpening high spatial resolution ratio images using optimization", Proc. SPIE 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications, 74980C (30 October 2009); https://doi.org/10.1117/12.832362

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KEYWORDS

Spatial resolution

Image fusion

Principal component analysis

Near infrared

Buildings

Image quality

Image resolution

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