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REVIGIS Résumé de rapport

Project ID: IST-1999-14189
Financé au titre de: FP5-IST
Pays: Netherlands

Map indices as measures for semantic data quality in segmented landsat images

This study addresses semantic accuracy in relation to images obtained with remote sensing. Semantic accuracy is defined in terms of map complexity. Map indices are applied as a metric to measure complexity. The idea is that a homogeneous map of a low complexity is of a high semantic accuracy. Complexity indices have been developed to quantify semantic issues like aggregation, fragmentation and patch size. In this study, these indices are applied on two images with different objectives, one from an agricultural area in the Netherlands, and one from a rural area in Kazakhstan. Images are segmented first using region merging segmentation. Effects on indices and semantic accuracy are discussed.

On the basis of well-defined subsets we conclude that the complexity indices are suitable to quantify the semantic accuracy of the map. Segmentation is the most useful for an agricultural area including various agricultural fields. The indices are mutually comparable being highly correlated, but showing on the other hand some different aspects in quantifying map homogeneity and identifying objects of a high semantic accuracy.


Alfred STEIN, (Head of Unit)
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