Contextual data fusion applied to forest map revision

Author

Solberg, Anne H Schistad

Author_Institution

Norwegian Comput. Center, Oslo, Norway

Volume

37

Issue

3

fYear

1999

fDate

5/1/1999 12:00:00 AM

Firstpage

1234

Lastpage

1243

Abstract

The use of a Markov random field model for multisource classification for map revision applications is investigated. A statistical model is presented, in which data from several remote sensing sensors is merged with spatial contextual information and a previous labeling of the scene from an existing thematic map to reach a consensus classification. The method is tested on two data sets for forest classification, and the classification performance is studied in terms of the effect of using remote sensing data from different sensors, the effect of spatial context, and the effect of using map data from previous surveys in the classification. It is shown that the use of a contextual classifier or an existing map of the area can have larger influence on the classification accuracy than using data from an additional sensor

Keywords

Markov processes; cartography; forestry; geophysical signal processing; geophysical techniques; image classification; remote sensing; sensor fusion; vegetation mapping; Markov random field model; cartography; consensus classification; context; contextual classifier; contextual data fusion; forest map revision; forestry; geophysical measurement technique; image classification; multisource classification; remote sensing; sensor fusion; spatial context; statistical model; vegetation mapping; Context modeling; Image segmentation; Labeling; Layout; Markov random fields; Multi-layer neural network; Neural networks; Remote sensing; Statistical analysis; Testing;

fLanguage

English

Journal_Title

Geoscience and Remote Sensing, IEEE Transactions on

Publisher

ieee

ISSN

0196-2892

Type

jour

DOI

10.1109/36.763280

Filename

763280