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Proceedings Paper

Urban land use/land cover mapping with high-resolution SAR imagery by integrating support vector machines into object-based analysis
Author(s): Hongtao Hu; Yifang Ban
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Paper Abstract

This paper investigates the capability of high-resolution SAR data for urban landuse/land-cover mapping by integrating support vector machines (SVMs) into object-based analysis. Five-date RADARSAT fine-beam C-HH SAR images with a pixel spacing of 6.25 meter were acquired over the rural-urban fringe of the Great Toronto Area (GTA) during May to August in 2002. First, the SAR images were segmented using multi-resolution segmentation algorithm and two segmentation levels were created. Next, a range of spectral, shape and texture features were selected and calculated for all image objects on both levels. The objects on the lower level then inherited features of their super objects. In this way, the objects on the lower level received detailed descriptions about their neighbours and contexts. Finally, SVM classifiers were used to classify the image objects on the lower level based on the selected features. For training the SVM, sample image objects on the lower level were used. One-against-one approach was chosen to apply SVM to multiclass classification of SAR images in this research. The results show that the proposed method can achieve a high accuracy for the classification of high-resolution SAR images over urban areas.

Paper Details

Date Published: 10 October 2008
PDF: 8 pages
Proc. SPIE 7110, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VIII, 71100K (10 October 2008); doi: 10.1117/12.800298
Show Author Affiliations
Hongtao Hu, Royal Institute of Technology (Sweden)
Yifang Ban, Royal Institute of Technology (Sweden)


Published in SPIE Proceedings Vol. 7110:
Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VIII
Ulrich Michel; Daniel L. Civco; Manfred Ehlers; Hermann J. Kaufmann, Editor(s)

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