Research Papers

Classifying segmented hyperspectral data from a heterogeneous urban environment using support vector machines

[+] Author Affiliations
Sebastian Van der Linden, Andreas Janz

Humboldt-Universita¨t zu Berlin

Bjorn Waske

Center for Remote Sensing of Land Surfaces

Michael Eiden

Research Centre Juelich

Patrick Hostert

Humboldt-Universita¨t zu Berlin

J. Appl. Remote Sens. 1(1), 013543 (October 26, 2007). doi:10.1117/1.2813466
History: Received March 28, 2007; Revised October 15, 2007; Accepted October 16, 2007; October 26, 2007; Online October 26, 2007
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Abstract

Classifying remotely sensed images from urban environments is challenging. Urban land cover classes are spectrally heterogeneous and materials from different classes have similar spectral properties. Image segmentation has become a common preprocessing step that helped to overcome such problems. However, little attention has been paid to impacts of segmentation on the data's spectral information content. Here, urban hyperspectral data is spectrally classified using support vector machines (SVM). By training a SVM on pixel information and applying it to the image before segmentation and after segmentation at different levels, the classification framework is maintained and the influence of the spectral generalization during image segmentation hence directly investigated. In addition, a straightforward multi-level approach was performed, which combines information from different levels into one final map. A stratified accuracy assessment by urban structure types is applied. The classification of the unsegmented data achieves an overall accuracy of 88.7%. Accuracy of the segment-based classification is lower and decreases with increasing segment size. Highest accuracies for the different urban structure types are achieved at varying segmentation levels. The accuracy of the multi-level approach is similar to that of unsegmented data but comprises the positive effects of more homogeneous segment-based classifications at different levels in one map.

© 2007 Society of Photo-Optical Instrumentation Engineers

Citation

Sebastian Van der Linden ; Andreas Janz ; Bjorn Waske ; Michael Eiden and Patrick Hostert
"Classifying segmented hyperspectral data from a heterogeneous urban environment using support vector machines", J. Appl. Remote Sens. 1(1), 013543 (October 26, 2007). ; http://dx.doi.org/10.1117/1.2813466


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