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Interpreting image-based methods for estimating the signal-to-noise ratio

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Interpreting image-based methods for estimating the signal-to-noise ratio. / Atkinson, Peter M.; Sargent, I. M. J.; Foody, Giles M. et al.
In: International Journal of Remote Sensing, Vol. 26, No. 22, 2005, p. 5099-5115.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Atkinson, PM, Sargent, IMJ, Foody, GM & Williams, J 2005, 'Interpreting image-based methods for estimating the signal-to-noise ratio', International Journal of Remote Sensing, vol. 26, no. 22, pp. 5099-5115. https://doi.org/10.1080/01431160500254999

APA

Atkinson, P. M., Sargent, I. M. J., Foody, G. M., & Williams, J. (2005). Interpreting image-based methods for estimating the signal-to-noise ratio. International Journal of Remote Sensing, 26(22), 5099-5115. https://doi.org/10.1080/01431160500254999

Vancouver

Atkinson PM, Sargent IMJ, Foody GM, Williams J. Interpreting image-based methods for estimating the signal-to-noise ratio. International Journal of Remote Sensing. 2005;26(22):5099-5115. doi: 10.1080/01431160500254999

Author

Atkinson, Peter M. ; Sargent, I. M. J. ; Foody, Giles M. et al. / Interpreting image-based methods for estimating the signal-to-noise ratio. In: International Journal of Remote Sensing. 2005 ; Vol. 26, No. 22. pp. 5099-5115.

Bibtex

@article{b5f874b192934b7fb39dcc2e3cb9aefc,
title = "Interpreting image-based methods for estimating the signal-to-noise ratio",
abstract = "The signal-to-noise ratio (SNR) of remotely sensed imagery has been estimated directly using a variety of image-based methods such as the Homogeneous Area (HA) and Geostatistical (GS) methods. However, previous research has shown that such estimates may be dependent on land cover type. We examine this dependence on land cover type using Compact Airborne Spectrographic Imager (CASI) imagery of an agricultural region in Falmouth, Cornwall. The SNR was estimated using the GS method for six different land covers and a range of wavelengths. Large differences in the SNR existed between land cover types. It follows that single estimates of SNR (e.g. for one land cover) should not be associated with an image (as a whole). It is recommended that either (i) each statistic is reported per land cover type per wavelength or (ii) that an image of local statistics is reported per wavelength. The regression of noise on signal can be used to separate independent noise (intercept) from signal-dependent noise (slope). Variation in the noise and SNR estimates can be used to (i) allow more accurate prediction of the SNR and (ii) provide information on uncertainty.",
author = "Atkinson, {Peter M.} and Sargent, {I. M. J.} and Foody, {Giles M.} and J. Williams",
note = "M1 - 20",
year = "2005",
doi = "10.1080/01431160500254999",
language = "English",
volume = "26",
pages = "5099--5115",
journal = "International Journal of Remote Sensing",
issn = "0143-1161",
publisher = "TAYLOR & FRANCIS LTD",
number = "22",

}

RIS

TY - JOUR

T1 - Interpreting image-based methods for estimating the signal-to-noise ratio

AU - Atkinson, Peter M.

AU - Sargent, I. M. J.

AU - Foody, Giles M.

AU - Williams, J.

N1 - M1 - 20

PY - 2005

Y1 - 2005

N2 - The signal-to-noise ratio (SNR) of remotely sensed imagery has been estimated directly using a variety of image-based methods such as the Homogeneous Area (HA) and Geostatistical (GS) methods. However, previous research has shown that such estimates may be dependent on land cover type. We examine this dependence on land cover type using Compact Airborne Spectrographic Imager (CASI) imagery of an agricultural region in Falmouth, Cornwall. The SNR was estimated using the GS method for six different land covers and a range of wavelengths. Large differences in the SNR existed between land cover types. It follows that single estimates of SNR (e.g. for one land cover) should not be associated with an image (as a whole). It is recommended that either (i) each statistic is reported per land cover type per wavelength or (ii) that an image of local statistics is reported per wavelength. The regression of noise on signal can be used to separate independent noise (intercept) from signal-dependent noise (slope). Variation in the noise and SNR estimates can be used to (i) allow more accurate prediction of the SNR and (ii) provide information on uncertainty.

AB - The signal-to-noise ratio (SNR) of remotely sensed imagery has been estimated directly using a variety of image-based methods such as the Homogeneous Area (HA) and Geostatistical (GS) methods. However, previous research has shown that such estimates may be dependent on land cover type. We examine this dependence on land cover type using Compact Airborne Spectrographic Imager (CASI) imagery of an agricultural region in Falmouth, Cornwall. The SNR was estimated using the GS method for six different land covers and a range of wavelengths. Large differences in the SNR existed between land cover types. It follows that single estimates of SNR (e.g. for one land cover) should not be associated with an image (as a whole). It is recommended that either (i) each statistic is reported per land cover type per wavelength or (ii) that an image of local statistics is reported per wavelength. The regression of noise on signal can be used to separate independent noise (intercept) from signal-dependent noise (slope). Variation in the noise and SNR estimates can be used to (i) allow more accurate prediction of the SNR and (ii) provide information on uncertainty.

U2 - 10.1080/01431160500254999

DO - 10.1080/01431160500254999

M3 - Journal article

VL - 26

SP - 5099

EP - 5115

JO - International Journal of Remote Sensing

JF - International Journal of Remote Sensing

SN - 0143-1161

IS - 22

ER -