By Topic

Backscattering and Statistical Information Fusion for Urban Area Mapping Using TerraSAR-X Data

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Houda Chaabouni-Chouayakh ; IMF, DLR, Munich, Oberpfaffenhofen, Germany ; Mihai Datcu

With the launch of the German TerraSAR-X system, a new generation of high-resolution spaceborne SAR data is available. This opens new perspectives and challenges for the automatic interpretation of urban environments. In fact, a rich information content, previously hidden or not clearly distinguishable in low resolution images such as urban structures (small buildings, vehicles, etc), is now disclosed. However, only proper approaches are able to retrieve automatically this new detailed information. This paper provides solutions for the semi-automatic interpretation and mapping of urban areas using the high resolution provided by TerraSAR-X data. Our solutions take into the increase, with the high resolution, of the visibility of some man-made structures whose scattering response has improved with the high frequency X-band SAR sensor carried by the TerraSAR-X system. They are mainly based on two steps. Firstly, we extract and describe two kinds of information: backscattering and statistical. Secondly, we propose to use information fusion techniques where intelligence has been introduced and enhanced in the way the different information is processed or treated, so that accurate mapping of urban areas could be reached. This mapping is performed through semantic categorization and retrieval of the different scene contents. Promising improvements and real progress toward automatic urban area mapping have been achieved using TerraSAR-X data.

Published in:

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  (Volume:3 ,  Issue: 4 )