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Optimal sampling for remote sensing: estimating the regional mean

Optimal sampling for remote sensing: estimating the regional mean
Optimal sampling for remote sensing: estimating the regional mean
The sampling of ground data cover has presented problems for many years. Investigators have found that they cannot afford the resources they require to achieve the precision that they desire. In a similar way, investigators today are finding that they cannot afford to process the vast volumes of data produced by remote sensing systems. It is thus likely that the image itself will need re-sampling. Thanks to regionalised variable theory there is now a means by which to optimise this sampling.
sampling, optimum, kriging, estimation variance, crop
1793-1796
Atkinson, P.M.
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Atkinson, P.M.
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Atkinson, P.M. (1988) Optimal sampling for remote sensing: estimating the regional mean. International Geoscience and Remote Sensing Symposium, 1988. IGARSS '88. Remote Sensing: Moving Toward the 21st Century. 12 - 16 Sep 1988. pp. 1793-1796 .

Record type: Conference or Workshop Item (Paper)

Abstract

The sampling of ground data cover has presented problems for many years. Investigators have found that they cannot afford the resources they require to achieve the precision that they desire. In a similar way, investigators today are finding that they cannot afford to process the vast volumes of data produced by remote sensing systems. It is thus likely that the image itself will need re-sampling. Thanks to regionalised variable theory there is now a means by which to optimise this sampling.

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More information

Published date: 1988
Venue - Dates: International Geoscience and Remote Sensing Symposium, 1988. IGARSS '88. Remote Sensing: Moving Toward the 21st Century, 1988-09-12 - 1988-09-16
Keywords: sampling, optimum, kriging, estimation variance, crop

Identifiers

Local EPrints ID: 17557
URI: https://eprints.soton.ac.uk/id/eprint/17557
PURE UUID: 9df52a30-3f71-4d1c-954f-09d7942f4761

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Date deposited: 18 Oct 2005
Last modified: 17 Jul 2017 16:38

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