Data fusion and multisource image classification

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.

Acknowledgment

The authors would like to acknowledge the Royal Society in awarding Dr D. Amarsaikhan funding to participate in an academic exchange at the University of Northumbria.

Notes

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Additional information

Notes on contributors

T. Douglas*

*Author to whom correspondence should be addressed.