Spatial distribution of the uncertainty in land cover maps obtained from remote sensing

X. Pons, E. Sevillano, G. Moré, P. Serra, D. Cornford, M. Ninyerola

Abstract

When combining remote sensing imagery with statistical classifiers to obtain categorical thematic maps it is not usual to provide data about the spatial distribution of the error and uncertainty of the resulting maps. This paper describes, in the context of GeoViQua FP7 project, feasible approaches for methods based on several steps such as hybrid classifiers. Both for “per pixel” and “per polygon” strategies, the proposal is based on the use of the available ground truth, which is used to properly model the spatial distribution of the errors. Results allow mapping the classification success with a very high level of reliability (R2>0,94), providing users a sound knowledge of the accuracy at every area of the map.


Keywords

hybrid classification; spatial distribution of uncertainty and error; Landsat; multivariate logistic regression; multivariate linear regression

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This journal is a Crossref Cited-by Linking member. This list shows the references that citing the article automatically, if there are. For more information about the system please visit Crossref site

1. Communicating Thematic Data Quality with Web Map Services
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ISPRS International Journal of Geo-Information  vol: 4  issue: 4  first page: 1965  year: 2015  
doi: 10.3390/ijgi4041965



 

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Universitat Politècnica de València

Official Journal of the Spanish Association of Remote Sensing

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