Empirical correction of multiple flightline hyperspectral aerial image mosaics

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Empirical correction of multiple flightline hyperspectral aerial image mosaics. / Asmat, A.; Milton, E. J.; Atkinson, Peter M.
In: Remote Sensing of Environment, Vol. 115, No. 10, 17.10.2011, p. 2664-2673.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Asmat, A, Milton, EJ & Atkinson, PM 2011, 'Empirical correction of multiple flightline hyperspectral aerial image mosaics', Remote Sensing of Environment, vol. 115, no. 10, pp. 2664-2673. https://doi.org/10.1016/j.rse.2011.05.022

APA

Asmat, A., Milton, E. J., & Atkinson, P. M. (2011). Empirical correction of multiple flightline hyperspectral aerial image mosaics. Remote Sensing of Environment, 115(10), 2664-2673. https://doi.org/10.1016/j.rse.2011.05.022

Vancouver

Asmat A, Milton EJ, Atkinson PM. Empirical correction of multiple flightline hyperspectral aerial image mosaics. Remote Sensing of Environment. 2011 Oct 17;115(10):2664-2673. Epub 2011 Jul 5. doi: 10.1016/j.rse.2011.05.022

Author

Asmat, A. ; Milton, E. J. ; Atkinson, Peter M. / Empirical correction of multiple flightline hyperspectral aerial image mosaics. In: Remote Sensing of Environment. 2011 ; Vol. 115, No. 10. pp. 2664-2673.

Bibtex

@article{88fc126512a244a988acd6391adf9318,

title = "Empirical correction of multiple flightline hyperspectral aerial image mosaics",

abstract = "Aerial survey provides the user with great flexibility in terms of the geometry of sensing and the timing of measurements, but mosaicking individual aerial images to produce an extensive coverage remains a problem. Empirical methods based on normalising individual images to a common standard are used widely to create visually acceptable mosaics. However, the effect of these methods on quantitative estimation of land surface properties is unknown. An existing method for atmospherically correcting an aerial image mosaic involves fitting a regression model using pixels from the overlapping edges of adjacent flightlines. Here, we demonstrate a new method of atmospherically correcting an aerial image mosaic, based on use of an additional orthogonal flightline. The two methods were compared by using the two image mosaics to calculate vegetation indices (NDVI, SAVI, ARVI), which were then used to predict leaf area index, which was known in detail from ground survey. The second method was found to have lower uncertainty for all three vegetation indices tested. ARVI was found to be the most robust of the three when applied across multiple flightlines, regardless of the method of atmospheric correction.",

keywords = "Image normalisation, Empirical radiometric correction, Vegetation indices",

author = "A. Asmat and Milton, {E. J.} and Atkinson, {Peter M.}",

note = "M1 - 10",

year = "2011",

month = oct,

day = "17",

doi = "10.1016/j.rse.2011.05.022",

language = "English",

volume = "115",

pages = "2664--2673",

journal = "Remote Sensing of Environment",

issn = "0034-4257",

publisher = "Elsevier Inc.",

number = "10",

}

RIS

TY - JOUR

T1 - Empirical correction of multiple flightline hyperspectral aerial image mosaics

AU - Asmat, A.

AU - Milton, E. J.

AU - Atkinson, Peter M.

N1 - M1 - 10

PY - 2011/10/17

Y1 - 2011/10/17

N2 - Aerial survey provides the user with great flexibility in terms of the geometry of sensing and the timing of measurements, but mosaicking individual aerial images to produce an extensive coverage remains a problem. Empirical methods based on normalising individual images to a common standard are used widely to create visually acceptable mosaics. However, the effect of these methods on quantitative estimation of land surface properties is unknown. An existing method for atmospherically correcting an aerial image mosaic involves fitting a regression model using pixels from the overlapping edges of adjacent flightlines. Here, we demonstrate a new method of atmospherically correcting an aerial image mosaic, based on use of an additional orthogonal flightline. The two methods were compared by using the two image mosaics to calculate vegetation indices (NDVI, SAVI, ARVI), which were then used to predict leaf area index, which was known in detail from ground survey. The second method was found to have lower uncertainty for all three vegetation indices tested. ARVI was found to be the most robust of the three when applied across multiple flightlines, regardless of the method of atmospheric correction.

AB - Aerial survey provides the user with great flexibility in terms of the geometry of sensing and the timing of measurements, but mosaicking individual aerial images to produce an extensive coverage remains a problem. Empirical methods based on normalising individual images to a common standard are used widely to create visually acceptable mosaics. However, the effect of these methods on quantitative estimation of land surface properties is unknown. An existing method for atmospherically correcting an aerial image mosaic involves fitting a regression model using pixels from the overlapping edges of adjacent flightlines. Here, we demonstrate a new method of atmospherically correcting an aerial image mosaic, based on use of an additional orthogonal flightline. The two methods were compared by using the two image mosaics to calculate vegetation indices (NDVI, SAVI, ARVI), which were then used to predict leaf area index, which was known in detail from ground survey. The second method was found to have lower uncertainty for all three vegetation indices tested. ARVI was found to be the most robust of the three when applied across multiple flightlines, regardless of the method of atmospheric correction.

KW - Image normalisation

KW - Empirical radiometric correction

KW - Vegetation indices

U2 - 10.1016/j.rse.2011.05.022

DO - 10.1016/j.rse.2011.05.022

M3 - Journal article

VL - 115

SP - 2664

EP - 2673

JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

SN - 0034-4257

IS - 10

ER -

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