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[1]LI Chun-hua.Component Substitution Pan Sharpening of High Resolution Remote Sensing Imagery[J].Research of Soil and Water Conservation,2014,21(03):109-115.

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Component Substitution Pan Sharpening of High Resolution Remote Sensing Imagery

Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume: 21 Number of periods: 2014 03 Page number: 109-115 Column: Public date: 2014-06-28

Title:: Component Substitution Pan Sharpening of High Resolution Remote Sensing Imagery

Author(s):: LI Chun-hua; College of Geography, Fujian Normal University, Fuzhou 350007, China

Keywords:: QuickBird imagery; component substitution pansharpening; Gram-Schmidt (GS) spectral sharpening; intensity-hue-saturation (IHS) transform; principal component analysis (PCA); spectral response function

CLC:: TP751.1

DOI:: -

Abstract:: Image fusion has great significance for image classification, feature extraction and feature identification. An effective image fusion technique can not only improve spatial details but also preserve the spectral information. A variety of image fusion techniques are devoted to merge multispectral (MS) and panchromatic (Pan) images. Among these techniques, component substitution (CS) methods are attractive because they are fast and easy to implement. But up to present in China, no study has been reported with respect to the in-depth analysis of the CS fusion principle. In this paper, we use linear algebra knowledge to analyze the principle of CS image fusion technique. Three CS based image fusion techniques are been compared, such as principal component Analysis (PCA), Gram-Schmidt transform pansharpening which simulates low resolution MS by spectral response function (GS1) and Gram-Schmidt transform pansharpening which simulates low resolution MS through multivariate regression of MS+Pan data (GS2). QuickBird image has been processed on the above three fusion algorithms. Experimental results show that although all these three algorithms have good spectral fidelity property, the GS2 algorithm is generally more efficient than PCA and GS1 algorithm. The spectral distortion is especially obvious in GS1 fused image. The red and NIR reflectance of vegetable surface features of GS1 fused image are obviously higher than that of original MS bands. Meanwhile, the green and NIR band spectral reflectance of high reflectance area (build-up land) of GS1 fused image are obviously lower than that of original MS bands. The reason to explain this phenomenon is that the GS1 method only considers the nominal spectral responses. Actually, the influence of other phenomena, such as on-orbit working conditions, variability of the observed scene, postprocessing effects, in particular, atmospheric influence can significantly modify the nominal spectral response. The GS2 method avoids this drawback by performing a linear regression between Pan and MS bands. The comparative analysis of these three methods shows that how to accurately simulate low resolution panchromatic band directly affects the spectral fidelity and how to construct a low-resolution panchromatic is the key technology in the current high-resolution remote sensing image fusion.

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