Mixed spectral-structural classification of very high resolution images with summation kernels
Details
Serval ID
serval:BIB_EFDDDDA9D497
Type
Inproceedings: an article in a conference proceedings.
Collection
Publications
Institution
Title
Mixed spectral-structural classification of very high resolution images with summation kernels
Title of the conference
Proceedings of SPIE: Image and signal processing for remote sensing XIV, Dresden, Germany, 23 - 26 September
Publication state
Published
Issued date
2008
Editor
Bruzzone L., Notarnicola C., Posa F.
Volume
7109
Pages
pp. 10
Language
english
Notes
Tuia2008f
Abstract
In this paper, mixed spectral-structural kernel machines are proposed
for the classification of very-high resolution images. The simultaneous
use of multispectral and structural features (computed using morphological
filters) allows a significant increase in classification accuracy
of remote sensing images. Subsequently, weighted summation kernel
support vector machines are proposed and applied in order to take
into account the multiscale nature of the scene considered. Such
classifiers use the Mercer property of kernel matrices to compute
a new kernel matrix accounting simultaneously for two scale parameters.
Tests on a Zurich QuickBird image show the relevance of the proposed
method : using the mixed spectral-structural features, the classification
accuracy increases of about 5%, achieving a Kappa index of 0.97.
The multikernel approach proposed provide an overall accuracy of
98.90% with related Kappa index of 0.985.
for the classification of very-high resolution images. The simultaneous
use of multispectral and structural features (computed using morphological
filters) allows a significant increase in classification accuracy
of remote sensing images. Subsequently, weighted summation kernel
support vector machines are proposed and applied in order to take
into account the multiscale nature of the scene considered. Such
classifiers use the Mercer property of kernel matrices to compute
a new kernel matrix accounting simultaneously for two scale parameters.
Tests on a Zurich QuickBird image show the relevance of the proposed
method : using the mixed spectral-structural features, the classification
accuracy increases of about 5%, achieving a Kappa index of 0.97.
The multikernel approach proposed provide an overall accuracy of
98.90% with related Kappa index of 0.985.
Create date
25/11/2013 18:18
Last modification date
20/08/2019 17:17
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