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Use of metatruth image concept to assess forest change detection accuracy at pixel level

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Traditionally, the validation of a classified multispectral image only quantifies its correspondence to ground reference data containing thematic information generalized at the stand level, with stands represented as vector polygons. Little is known of the accuracy of such classifications at a scale below the stand. This study presents a methodology to assess classification accuracy at pixel level, i.e. sub-polygon, where the classification procedure is embedded in a change detection environment. A new type of reference data (Metatruth Image) was generated based on the integration of the outputs of various independent change detection procedures. The integration consisted of calculating for each pixel a probability distribution or pixel purity index for each change class by independent change detection procedures, defined by the number of times the pixel has been classified as a certain change class. First, the relationship between purity and accuracy was successfully validated. Next, the Metatruth Image was created based on 'high purity pixels'. Performing traditional accuracy assessment on the outputs of individual change detection procedures using the Metatruth Image as reference dataset, demonstrated that former outputs identified change events accurately at pixel level. As a consequence, traditional accuracy assessment at polygon level underestimates the true accuracy at pixel level of the change detection procedure in a systematic way with differences in kappa coefficients of agreement around 20%.
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Document Type: Research Article

Affiliations: Laboratory for Forest Nature and Landscape Research Vital Decosterstraat 102 3000 Leuven Belgium

Publication date: July 1, 2004

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