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Estimating per-pixel thematic uncertainty in remote sensing classifications

Estimating per-pixel thematic uncertainty in remote sensing classifications
Estimating per-pixel thematic uncertainty in remote sensing classifications
Standard methodologies for estimating the thematic accuracy of hard classifications, such as those using the confusion matrix, do not provide indications of where thematic errors occur. However, spatial variation in thematic error can be a key variable affecting output errors when operations such as change detection are applied. One method of assessing thematic error on a per-pixel basis is to use the outputs of a classifier to estimate thematic uncertainty. Previous studies that have used this approach have generally used a single classifier and so comparisons of the relative accuracy of classifiers for predicting per-pixel thematic uncertainty have not been made. This paper compared three classification methods for predicting thematic uncertainty: the maximum likelihood, the multi-layer perceptron and the probabilistic neural network. The results of the study are discussed in terms of selecting the most suitable classifier for mapping land cover or predicting thematic uncertainty.

0143-1161
209
Brown, K.M.
db289f77-f6e6-4caa-9c0d-7bf80fdd69ab
Foody, G.M.
06e50027-603d-4a5b-88f5-af2bb6235a37
Atkinson, P.M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Brown, K.M.
db289f77-f6e6-4caa-9c0d-7bf80fdd69ab
Foody, G.M.
06e50027-603d-4a5b-88f5-af2bb6235a37
Atkinson, P.M.
96e96579-56fe-424d-a21c-17b6eed13b0b

Brown, K.M., Foody, G.M. and Atkinson, P.M. (2009) Estimating per-pixel thematic uncertainty in remote sensing classifications. International Journal of Remote Sensing, 30 (1), 209. (doi:10.1080/01431160802290568).

Record type: Article

Abstract

Standard methodologies for estimating the thematic accuracy of hard classifications, such as those using the confusion matrix, do not provide indications of where thematic errors occur. However, spatial variation in thematic error can be a key variable affecting output errors when operations such as change detection are applied. One method of assessing thematic error on a per-pixel basis is to use the outputs of a classifier to estimate thematic uncertainty. Previous studies that have used this approach have generally used a single classifier and so comparisons of the relative accuracy of classifiers for predicting per-pixel thematic uncertainty have not been made. This paper compared three classification methods for predicting thematic uncertainty: the maximum likelihood, the multi-layer perceptron and the probabilistic neural network. The results of the study are discussed in terms of selecting the most suitable classifier for mapping land cover or predicting thematic uncertainty.

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Published date: 2009

Identifiers

Local EPrints ID: 142883
URI: https://eprints.soton.ac.uk/id/eprint/142883
ISSN: 0143-1161
PURE UUID: 3e23a64e-96be-41ae-942c-728a5f19769f

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Date deposited: 01 Apr 2010 12:57
Last modified: 18 Jul 2017 23:10

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