A comparison of pixel- and object-level data fusion using lidar and high-resolution imagery for enhanced classification

Ali, S, Dare, P and Jones, S 2009, 'A comparison of pixel- and object-level data fusion using lidar and high-resolution imagery for enhanced classification' in Simon Jones, Karin Reinke (ed.) Innovations in remote sensing and photogrammetry (Lecture notes in Geoinformation and Cartography), Springer, Heidelberg, Germany, pp. 3-18.


Document type: Book Chapter
Collection: Book Chapters

Title A comparison of pixel- and object-level data fusion using lidar and high-resolution imagery for enhanced classification
Author(s) Ali, S
Dare, P
Jones, S
Year 2009
Title of book Innovations in remote sensing and photogrammetry (Lecture notes in Geoinformation and Cartography)
Publisher Springer
Place of publication Heidelberg, Germany
Editor(s) Simon Jones, Karin Reinke
Start page 3
End page 18
Subjects Studies in Creative Arts and Writing
Summary Fusion of multisource data is becoming a widely used procedure due to the availability of complementary yet dissimilar datasets. The combined use of high spatial resolution imagery and lidar (light detection and ranging) derived digital surface models (DSM) can reduce interclass confusion in the fusion process. However, pixel-level data fusion does not take spatial information into account. Pixels from multisource images are fused depending on their spectral values, regardless of their neighbour values. Object-level fusion overcomes this shortcoming by segmenting multisource images into meaningful objects and then performing fusion with the information imbedded into their topology. This paper compares the results of the pixel- and object-level fusion of a lidar derived DSM with colour aerial photography and multispectral imagery. The comparison is based on the assessment of the classification accuracy where reference information has been collected through field survey. Pixel-level fusion of the colour photography and the DSM exhibits better results than sole classification of colour photography. The same result is found for the multispectral imagery and the DSM. Object-level fusion achieves superior results compared to all pixel-level classification of tested categories. Object-level fusion of the colour photography and the DSM shows the highest classification accuracy (91%).
Copyright notice © Springer-Verlag Berlin Heidelberg 2009
Keyword(s) remote sensing
DOI - identifier 10.1007/978-3-540-93962-7_1
ISBN 9783540882657
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