Data fusion and multisource image classification

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Abstract:

The aim of this study is to explore different data fusion techniques and compare the performances of a standard supervised classification and expert classification. For the supervised classification, different feature extraction approaches are used. To increase the reliability of the classification, different threshold values are determined and fuzzy convolutions are applied. For the expert classification, a set of rules is determined and a hierarchical decision tree is created. Overall, the research indicates that multisource information can significantly improve the interpretation and classification of land cover types and the expert classification is a powerful tool in the production of a reliable land cover map.

Document Type: Research Article

DOI: http://dx.doi.org/10.1080/0143116031000115111

Affiliations: 1: Department of Geoinformatics, Institute of Informatics and RS Mongolian Academy of Sciences Ulaanbaatar-51 Mongolia, Email: amarsaikhan_d@arvis.ac.mn 2: School of Applied Sciences University of Northumbria Newcastle upon Tyne England UK, Email: terry.douglas@northumbria.ac.uk

Publication date: September 1, 2004

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