Estimation of signal-to-noise: a new procedure applied to AVIRIS data
To make the best use of narrowband Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data, an investigator needs to know the signal-to-noise ratio (SNR). The signal is land cover dependent and varies with both wavelength and atmospheric absorption, and random noise comprises sensor noise and i...
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| Published in | IEEE transactions on geoscience and remote sensing Vol. 27; no. 5; pp. 620 - 628 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
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IEEE
01.09.1989
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0196-2892 |
| DOI | 10.1109/TGRS.1989.35945 |
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| Abstract | To make the best use of narrowband Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data, an investigator needs to know the signal-to-noise ratio (SNR). The signal is land cover dependent and varies with both wavelength and atmospheric absorption, and random noise comprises sensor noise and intrapixel variability (i.e. variability within a pixel). The three existing methods for estimating the SNR are inadequate, since typical laboratory methods inflate, while typical dark-current and image methods deflate the SNR value. The authors propose a procedure called the geostatistical method that is based on the removal of periodic noise by notch filtering in the frequency domain and the isolation of sensor noise and intrapixel variability using the semivariogram. This procedure was applied easily and successfully to five sets of AVIRIS data from the 1987 flying season and could be applied to remotely sensed data from broadband sensors.< > |
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| AbstractList | To make the best use of narrowband airborne visible/infrared imaging spectrometer (AVIRIS) data, an investigator needs to know the ratio of signal to random variability or noise (signal-to-noise ratio or SNR). The signal is land cover dependent and varies with both wavelength and atmospheric absorption; random noise comprises sensor noise and intrapixel variability (i.e., variability within a pixel). The three existing methods for estimating the SNR are inadequate, since typical laboratory methods inflate while dark current and image methods deflate the SNR. A new procedure is proposed called the geostatistical method. It is based on the removal of periodic noise by notch filtering in the frequency domain and the isolation of sensor noise and intrapixel variability using the semi- variogram. This procedure was applied easily and successfully to five sets of AVIRIS data from the 1987 flying season and could be applied to remotely sensed data from broadband sensors. (Author) To make the best use of narrowband Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data, an investigator needs to know the signal-to-noise ratio (SNR). The signal is land cover dependent and varies with both wavelength and atmospheric absorption, and random noise comprises sensor noise and intrapixel variability (i.e. variability within a pixel). The three existing methods for estimating the SNR are inadequate, since typical laboratory methods inflate, while typical dark-current and image methods deflate the SNR value. The authors propose a procedure called the geostatistical method that is based on the removal of periodic noise by notch filtering in the frequency domain and the isolation of sensor noise and intrapixel variability using the semivariogram. This procedure was applied easily and successfully to five sets of AVIRIS data from the 1987 flying season and could be applied to remotely sensed data from broadband sensors To make the best use of narrowband Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data, an investigator needs to know the ratio of singal to random variability or "noise" (signal-to-noise ratio or SNR). The three existing methods for estimating the SNR are inadequate, since typical "laboratory" methods inflate, while typical "dark current" and "image" methods deflate, the SNR. We propose a new procedure called the "geostatistical" method. It is based on the removal of periodic noise by "notch filtering" in the frequency domain and the isolation of sensor noise and intrapixel variability using the semivariogram. To make the best use of narrowband Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data, an investigator needs to know the signal-to-noise ratio (SNR). The signal is land cover dependent and varies with both wavelength and atmospheric absorption, and random noise comprises sensor noise and intrapixel variability (i.e. variability within a pixel). The three existing methods for estimating the SNR are inadequate, since typical laboratory methods inflate, while typical dark-current and image methods deflate the SNR value. The authors propose a procedure called the geostatistical method that is based on the removal of periodic noise by notch filtering in the frequency domain and the isolation of sensor noise and intrapixel variability using the semivariogram. This procedure was applied easily and successfully to five sets of AVIRIS data from the 1987 flying season and could be applied to remotely sensed data from broadband sensors.< > To make the best use of narrowband airborne visible/infrared imaging spectrometer (AVIRIS) data, an investigator needs to know the ratio of signal to random variability or noise (signal-to-noise ratio or SNR). The signal is land cover dependent and varies with both wavelength and atmospheric absorption; random noise comprises sensor noise and intrapixel variability (i.e., variability within a pixel). The three existing methods for estimating the SNR are inadequate, since typical laboratory methods inflate while dark current and image methods deflate the SNR. A new procedure is proposed called the geostatistical method. It is based on the removal of periodic noise by notch filtering in the frequency domain and the isolation of sensor noise and intrapixel variability using the semi-variogram. This procedure was applied easily and successfully to five sets of AVIRIS data from the 1987 flying season and could be applied to remotely sensed data from broadband sensors. |
| Audience | PUBLIC |
| Author | Dungan, J.L. Curran, P.J. |
| Author_xml | – sequence: 1 givenname: P.J. surname: Curran fullname: Curran, P.J. organization: NASA Ames Res. Center, Moffett Field, CA, USA – sequence: 2 givenname: J.L. surname: Dungan fullname: Dungan, J.L. |
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| Cites_doi | 10.1117/12.942288 10.1016/0034-4257(88)90007-7 10.1016/0034-4257(88)90021-1 10.1016/0034-4257(88)90003-X 10.1109/TGRS.1984.350620 10.1109/36.3050 10.1007/978-1-4612-5090-6_1 10.1117/12.942295 10.1038/335154a0 10.1126/science.228.4704.1147 10.1117/12.942294 10.1117/12.942280 |
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| References_xml | – ident: ref31 doi: 10.1117/12.942288 – year: 1987 ident: ref5 publication-title: Airborne visible/infrared imaging spectrometer AVIRIS A description of the sensor ground data processing facility laboratory calibration and first results – start-page: 162 year: 1988 ident: ref19 article-title: Determination of in-flight AVIRIS spectral, radiometric, spectral and signal-to-noise characteristics using atmospheric and surface measurements from the vicinity of the rare-earth-bearing carbonatite at Mountain Pass, California publication-title: Proceedings of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Performance Evaluation Workshop – ident: ref11 doi: 10.1016/0034-4257(88)90007-7 – year: 1969 ident: ref14 publication-title: Radiation in the Atmosphere – ident: ref25 doi: 10.1016/0034-4257(88)90021-1 – start-page: 70 year: 1987 ident: ref41 article-title: The use of airborne imaging spectrometer data to determine experimentally induced variation in 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| SubjectTerms | Absorption Earth Resources And Remote Sensing Infrared imaging Infrared spectra Layout NASA Noise level Reflectivity Sensor phenomena and characterization Signal to noise ratio Spectroscopy |
| Title | Estimation of signal-to-noise: a new procedure applied to AVIRIS data |
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