MAP estimation for hyperspectral image resolution enhancement using an auxiliary sensor
This paper presents a novel maximum a posteriori estimator for enhancing the spatial resolution of an image using co-registered high spatial-resolution imagery from an auxiliary sensor. Here, we focus on the use of high-resolution panchromatic data to enhance hyperspectral imagery. However, the esti...
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| Published in: | IEEE transactions on image processing Vol. 13; no. 9; pp. 1174 - 1184 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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New York, NY
IEEE
01.09.2004
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| ISSN: | 1057-7149, 1941-0042 |
| Online Access: | Get full text |
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| Abstract | This paper presents a novel maximum a posteriori estimator for enhancing the spatial resolution of an image using co-registered high spatial-resolution imagery from an auxiliary sensor. Here, we focus on the use of high-resolution panchromatic data to enhance hyperspectral imagery. However, the estimation framework developed allows for any number of spectral bands in the primary and auxiliary image. The proposed technique is suitable for applications where some correlation, either localized or global, exists between the auxiliary image and the image being enhanced. To exploit localized correlations, a spatially varying statistical model, based on vector quantization, is used. Another important aspect of the proposed algorithm is that it allows for the use of an accurate observation model relating the "true" scene with the low-resolutions observations. Experimental results with hyperspectral data derived from the airborne visible-infrared imaging spectrometer are presented to demonstrate the efficacy of the proposed estimator. |
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| AbstractList | This paper presents a novel maximum a posteriori estimator for enhancing the spatial resolution of an image using co-registered high spatial-resolution imagery from an auxiliary sensor. Here, we focus on the use of high-resolution panchomatic data to enhance hyperspectral imagery. However, the estimation framework developed allows for any number of spectral bands in the primary and auxiliary image. The proposed technique is suitable for applications where some correlation, either localized or global, exists between the auxiliary image and the image being enhanced. To exploit localized correlations, a spatially varying statistical model, based on vector quantization, is used. Another important aspect of the proposed algorithm is that it allows for the use of an accurate observation model relating the "true" scene with the low-resolutions observations. Experimental results with hyperspectral data derived from the airborne visible-infrared imaging spectrometer are presented to demonstrate the efficacy of the proposed estimator. This paper presents a novel maximum a posteriori estimator for enhancing the spatial resolution of an image using co-registered high spatial-resolution imagery from an auxiliary sensor. Here, we focus on the use of high-resolution panchomatic data to enhance hyperspectral imagery. However, the estimation framework developed allows for any number of spectral bands in the primary and auxiliary image. The proposed technique is suitable for applications where some correlation, either localized or global, exists between the auxiliary image and the image being enhanced. To exploit localized correlations, a spatially varying statistical model, based on vector quantization, is used. Another important aspect of the proposed algorithm is that it allows for the use of an accurate observation model relating the "true" scene with the low-resolutions observations. Experimental results with hyperspectral data derived from the airborne visible-infrared imaging spectrometer are presented to demonstrate the efficacy of the proposed estimator.This paper presents a novel maximum a posteriori estimator for enhancing the spatial resolution of an image using co-registered high spatial-resolution imagery from an auxiliary sensor. Here, we focus on the use of high-resolution panchomatic data to enhance hyperspectral imagery. However, the estimation framework developed allows for any number of spectral bands in the primary and auxiliary image. The proposed technique is suitable for applications where some correlation, either localized or global, exists between the auxiliary image and the image being enhanced. To exploit localized correlations, a spatially varying statistical model, based on vector quantization, is used. Another important aspect of the proposed algorithm is that it allows for the use of an accurate observation model relating the "true" scene with the low-resolutions observations. Experimental results with hyperspectral data derived from the airborne visible-infrared imaging spectrometer are presented to demonstrate the efficacy of the proposed estimator. This paper presents a novel maximum a posteriori estimator for enhancing the spatial resolution of an image using co-registered high spatial-resolution imagery from an auxiliary sensor. Here, we focus on the use of high-resolution panchromatic data to enhance hyperspectral imagery. However, the estimation framework developed allows for any number of spectral bands in the primary and auxiliary image. The proposed technique is suitable for applications where some correlation, either localized or global, exists between the auxiliary image and the image being enhanced. To exploit localized correlations, a spatially varying statistical model, based on vector quantization, is used. Another important aspect of the proposed algorithm is that it allows for the use of an accurate observation model relating the "true" scene with the low-resolutions observations. Experimental results with hyperspectral data derived from the airborne visible-infrared imaging spectrometer are presented to demonstrate the efficacy of the proposed estimator. |
| Author | Eismann, M.T. Wilson, G.L. Hardie, R.C. |
| Author_xml | – sequence: 1 givenname: R.C. surname: Hardie fullname: Hardie, R.C. organization: Comput. Eng. & Electro-Opt. Program, Univ. of Dayton, USA – sequence: 2 givenname: M.T. surname: Eismann fullname: Eismann, M.T. – sequence: 3 givenname: G.L. surname: Wilson fullname: Wilson, G.L. |
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| Keywords | Parameter estimation maximum a posteriori (MAP) estimation Multisensor Vector quantization Image processing Hyperspectral resolution enhancement multispectral Hyperspectral imaging sensor Statistical model A posteriori estimation panchromatic sharpening Spatial resolution Image enhancement |
| Language | English |
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| References | Chavez (ref4) 1991; 57 ref13 Munechika (ref6) 1993; 59 ref12 ref15 ref14 ref20 Shettigara (ref5) 1992; 58 ref11 Schowengerdt (ref1) 1980; 46 ref10 Ranchin (ref16) 2000; 66 ref21 ref2 Carper (ref3) 1990; 56 Kay (ref17) 1993 ref19 ref18 ref8 ref7 ref9 Porter (ref22) 1990; 1298 |
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| SubjectTerms | Algorithms Applied sciences Artificial intelligence Computer science; control theory; systems Computer Simulation Correlation Effectiveness Estimators Exact sciences and technology Hyperspectral imaging Hyperspectral sensors Image Enhancement - instrumentation Image Enhancement - methods Image Interpretation, Computer-Assisted - instrumentation Image Interpretation, Computer-Assisted - methods Image processing Image resolution Image sensors Imagery Information Storage and Retrieval - methods Information, signal and communications theory Layout Maximum a posteriori estimation Models, Statistical Numerical Analysis, Computer-Assisted Optical imaging Pattern Recognition, Automated Pattern recognition. Digital image processing. Computational geometry Reproducibility of Results Sensitivity and Specificity Sensors Signal processing Signal Processing, Computer-Assisted Spatial resolution Spectral bands Spectroscopy Subtraction Technique Telecommunications and information theory Transducers Vector quantization |
| Title | MAP estimation for hyperspectral image resolution enhancement using an auxiliary sensor |
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