Improving Component Substitution Pansharpening Through Multivariate Regression of MS +Pan Data
In this paper, multivariate regression is adopted to improve spectral quality, without diminishing spatial quality, in image fusion methods based on the well-established component substitution (CS) approach. A general scheme that is capable of modeling any CS image fusion method is presented and dis...
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Published in | IEEE transactions on geoscience and remote sensing Vol. 45; no. 10; pp. 3230 - 3239 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
New York
IEEE
01.10.2007
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
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Abstract | In this paper, multivariate regression is adopted to improve spectral quality, without diminishing spatial quality, in image fusion methods based on the well-established component substitution (CS) approach. A general scheme that is capable of modeling any CS image fusion method is presented and discussed. According to this scheme, a generalized intensity component is defined as the weighted average of the multispectral (MS) bands. The weights are obtained as regression coefficients between the MS bands and the spatially degraded panchromatic (Pan) image, with the aim of capturing the spectral responses of the sensors. Once it has been integrated into the Gram-Schmidt spectral-sharpening method, which is implemented in environment for visualizing images (ENVI) program, and into the generalized intensity-hue-saturation fusion method, the proposed preprocessing module allows the production of fused images of the same spatial sharpness but of increased spectral quality with respect to the standard implementations. In addition, quantitative scores carried out on spatially degraded data clearly confirm the superiority of the enhanced methods over their baselines. |
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AbstractList | In this paper, multivariate regression is adopted to improve spectral quality, without diminishing spatial quality, in image fusion methods based on the well-established component substitution (CS) approach. A general scheme that is capable of modeling any CS image fusion method is presented and discussed. According to this scheme, a generalized intensity component is defined as the weighted average of the multispectral (MS) bands. The weights are obtained as regression coefficients between the MS bands and the spatially degraded panchromatic (Pan) image, with the aim of capturing the spectral responses of the sensors. Once it has been integrated into the Gram-Schmidt spectral-sharpening method, which is implemented in environment for visualizing images (ENVI) program, and into the generalized intensity-hue-saturation fusion method, the proposed preprocessing module allows the production of fused images of the same spatial sharpness but of increased spectral quality with respect to the standard implementations. In addition, quantitative scores carried out on spatially degraded data clearly confirm the superiority of the enhanced methods over their baselines. According to this scheme, a generalized intensity component is defined as the weighted average of the multispectral (MS) bands. |
Author | Selva, M. Aiazzi, B. Baronti, S. |
Author_xml | – sequence: 1 givenname: B. surname: Aiazzi fullname: Aiazzi, B. organization: Inst. of Appl. Phys. Nello Carrara CNR Area di Ricerca di Firenze, Florence – sequence: 2 givenname: S. surname: Baronti fullname: Baronti, S. organization: Inst. of Appl. Phys. Nello Carrara CNR Area di Ricerca di Firenze, Florence – sequence: 3 givenname: M. surname: Selva fullname: Selva, M. organization: Inst. of Appl. Phys. Nello Carrara CNR Area di Ricerca di Firenze, Florence |
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PublicationTitle | IEEE transactions on geoscience and remote sensing |
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References | ref13 ref15 chavez (ref5) 1991; 57 ref10 ref1 lloyd (ref25) 1997 ref17 ref16 ref19 ref18 ranchin (ref14) 2000; 66 shettigara (ref7) 1992; 58 (ref12) 2000 zhang (ref11) 2004; 70 ref24 ref20 carper (ref8) 1990; 56 wahlen (ref2) 2002 ref21 ref9 ref4 ref6 ross (ref22) 2004 zang (ref3) 2004 wald (ref23) 1997; 63 |
References_xml | – ident: ref17 doi: 10.14358/PERS.72.5.591 – ident: ref20 doi: 10.1109/TGRS.2004.838344 – volume: 57 start-page: 295 year: 1991 ident: ref5 article-title: comparison of three different methods to merge multiresolution and multispectral data: landsat tm and spot panchromatic publication-title: Photogramm Eng Remote Sens contributor: fullname: chavez – volume: 56 start-page: 459 year: 1990 ident: ref8 article-title: the use of intensity-hue-saturation transformations for merging spot panchromatic and multi-spectral image data publication-title: Photogramm Eng Remote Sens contributor: fullname: carper – ident: ref19 doi: 10.1016/S1566-2535(01)00036-7 – ident: ref13 doi: 10.1109/36.763274 – volume: 63 start-page: 691 year: 1997 ident: ref23 article-title: fusion of satellite images of different spatial resolutions: assessing the quality of resulting images publication-title: Photogramm Eng Remote Sens contributor: fullname: wald – ident: ref6 doi: 10.1109/TGRS.2005.846874 – ident: ref4 doi: 10.1080/01431160600649254 – ident: ref10 doi: 10.1109/TGRS.2005.863299 – year: 2002 ident: ref2 publication-title: Data Mining III contributor: fullname: wahlen – year: 2000 ident: ref12 publication-title: Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening – start-page: 960 year: 2004 ident: ref3 article-title: multi-resolution and multi-spectral image fusion for urban object extraction publication-title: Proc 20th ISPRS Congr contributor: fullname: zang – ident: ref1 doi: 10.1109/36.763269 – volume: 70 start-page: 657 year: 2004 ident: ref11 article-title: understanding image fusion publication-title: Photogramm Eng Remote Sens contributor: fullname: zhang – ident: ref9 doi: 10.1109/LGRS.2004.834804 – ident: ref16 doi: 10.1016/S0924-2716(03)00013-3 – year: 1997 ident: ref25 publication-title: Numerical Linear Algebra contributor: fullname: lloyd – year: 2004 ident: ref22 publication-title: Introduction to Probability and Statistics for Engineers and Scientists contributor: fullname: ross – ident: ref15 doi: 10.1109/TGRS.2002.803623 – volume: 58 start-page: 561 year: 1992 ident: ref7 article-title: a generalized component substitution technique for spatial enhancement of multispectral images using a higher resolution data set publication-title: Photogramm Eng Remote Sens contributor: fullname: shettigara – ident: ref24 doi: 10.1109/LGRS.2004.836784 – volume: 66 start-page: 49 year: 2000 ident: ref14 article-title: fusion of high spatial and spectral resolution images: the arsis concept and its implementation publication-title: Photogramm Eng Remote Sens contributor: fullname: ranchin – ident: ref21 doi: 10.1109/TGRS.2005.856106 – ident: ref18 doi: 10.1109/TGRS.2004.825593 |
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Snippet | In this paper, multivariate regression is adopted to improve spectral quality, without diminishing spatial quality, in image fusion methods based on the... According to this scheme, a generalized intensity component is defined as the weighted average of the multispectral (MS) bands. |
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SubjectTerms | Band spectra Bands Component substitution (CS) pansharpening Data visualization Degradation Gram-Schmidt (GS) spectral sharpening IKONOS satellite data Image fusion Image resolution Image sensors intensity-hue-saturation (IHS) transform Mathematical models multispectral (MS) imagery Multivariate regression Preprocessing Principal component analysis QuickBird images Radiometry Regression Satellites Spatial resolution Spectra Studies |
Title | Improving Component Substitution Pansharpening Through Multivariate Regression of MS +Pan Data |
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