A generalized confusion matrix for assessing area estimates from remote sensing imagery
A generalized confusion matrix for assessing area estimates from remote sensing imagery
The formulation of a generalized area-based confusion matrix for exploring the accuracy of area estimates is presented. The generalized confusion matrix is appropriate for both traditional classification algorithms and sub-pixel area estimation models. An error matrix, derived from the generalized confusion matrix, allows the accuracy of maps generated using area estimation models to be assessed quantitatively and compared to the accuracies obtained from traditional classification techniques. The application of this approach is demonstrated for an area estimation model applied to Landsat data of an urban area of the United Kingdom.
3223-3235
Lewis, H.G.
e9048cd8-c188-49cb-8e2a-45f6b316336a
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
2001
Lewis, H.G.
e9048cd8-c188-49cb-8e2a-45f6b316336a
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
Lewis, H.G. and Brown, M.
(2001)
A generalized confusion matrix for assessing area estimates from remote sensing imagery.
International Journal of Remote Sensing, 22 (16), .
(doi:10.1080/01431160152558332).
Abstract
The formulation of a generalized area-based confusion matrix for exploring the accuracy of area estimates is presented. The generalized confusion matrix is appropriate for both traditional classification algorithms and sub-pixel area estimation models. An error matrix, derived from the generalized confusion matrix, allows the accuracy of maps generated using area estimation models to be assessed quantitatively and compared to the accuracies obtained from traditional classification techniques. The application of this approach is demonstrated for an area estimation model applied to Landsat data of an urban area of the United Kingdom.
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Published date: 2001
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Local EPrints ID: 21972
URI: http://eprints.soton.ac.uk/id/eprint/21972
ISSN: 0143-1161
PURE UUID: 04e13f53-d39f-4440-8e11-f51afee87b38
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Date deposited: 16 Mar 2006
Last modified: 16 Mar 2024 02:55
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M. Brown
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