Fusing high-resolution SAR and optical imagery for improved urban land cover study and classification

Autor(en)

D. Amarsaikhan, Hans-Heinrich Blotevogel, J.L. Van Genderen, M. Ganzorig, R. Gantuya, B. Nergui

Abstrakt

The two objectives of this study are to compare the performances of different data fusion techniques for the enhancement of urban features and subsequently to improve urban land cover types classification using a refined Bayesian classification. For the data fusion, wavelet-based fusion, Brovey transform, Elhers fusion and principal component analysis are used and the results are compared. The refined Bayesian classification uses spatial thresholds defined from local knowledge and different features obtained through a feature derivation process. The result of the refined classification is compared with the results of a standard method and it demonstrates a higher accuracy. Overall, the research indicates that multi-source information can significantly improves the interpretation and classification of land cover types and the refined Bayesian classification is a powerful tool to increase the classification accuracy.

Organisation(en)

Externe Organisation(en)

Technische Universität Dortmund, Mongolian Academy of Sciences (MAS), University of Twente

Journal

International Journal of Image and Data Fusion

Band

1

Seiten

83-97

ISSN

1947-9832

DOI

https://doi.org/10.1080/19479830903562041

Publikationsdatum

2010

Peer-reviewed

Ja

ÖFOS 2012

507012 Raumordnung

Link zum Portal

https://ucrisportal.univie.ac.at/de/publications/c888e046-3bd0-456a-a9e5-63d0c6d75ed6