DocumentCode :
2206478
Title :
Domain adaptation for the extraction of complex urban patterns from multiresolution satellite images
Author :
Kurtz, Camille ; Puissant, Anne ; Passat, Nicolas ; Gançarski, Pierre
Author_Institution :
LSIIT, Univ. of Strasbourg, Strasbourg, France
fYear :
2012
fDate :
22-27 July 2012
Firstpage :
1773
Lastpage :
1776
Abstract :
The extraction of complex urban patterns from Very High Spatial Resolution (VHSR) images presents several challenges related to the complexity of the data. Based on the availability of images of a same scene at various resolutions (Medium to Very High Spatial resolutions), a hierarchical approach has been recently proposed to segment/classify objects of interest in a top-down fashion in order to determine patterns from VHSR images. To perform, this method requires the interactive definition of segmentation examples for each considered resolution image. In the context of large dataset processing, such interactive task becomes time consuming. To deal with this issue, we propose in this article, an extension of the domain adaptation paradigm enabling the transfer of the segmentation examples defined on a source dataset to automatically process a target one. Experiments performed on urban images provide satisfactory results which may be further used for operational needs.
Keywords :
geophysical image processing; geophysical techniques; image classification; image segmentation; remote sensing; VHSR images; complex urban pattern extraction; data complexity; domain adaptation; large dataset processing; multiresolution satellite images; object classification; object segmentation; urban images; very high spatial resolution images; Data mining; Image segmentation; Indexes; Remote sensing; Satellites; Spatial resolution; Clustering; Domain adaptation; Hierarchical segmentation; Multiresolution satellite images; Urban analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International
Conference_Location :
Munich
ISSN :
2153-6996
Print_ISBN :
978-1-4673-1160-1
Electronic_ISBN :
2153-6996
Type :
conf
DOI :
10.1109/IGARSS.2012.6351172
Filename :
6351172
Link To Document :
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