TY - GEN
T1 - Evaluation of a statistical fusion of linear features in sar data
AU - Hedman, Karin
AU - Hinz, Stefan
AU - Stilla, Uwe
N1 - Funding Information:
This study was supported by the National Institute of Environmental Health Sciences Grants ES000002 and ES015172-01, and the U.S. Environmental Protection Agency Grant RD-834798-01. Dr. David Sparrow was supported by a VA Research Career Scientist Award. Dr. Avron Spiro was supported by a VA CSR&D Senior Research Career Scientist Award. The VA Normative Aging Study is supported by the Cooperative Studies Program/Epidemiology Research and Information Centers of the U.S. Department of Veterans Affairs and is a component of the Massachusetts Veterans Epidemiology Research and Information Center, Boston, Massachusetts. Disclaimers: The contents are solely the responsibility of the Grantee and do not necessarily represent the official views of the funders. Further, the funders do not endorse the purchase of any commercial products or services mentioned in the publication.
PY - 2008
Y1 - 2008
N2 - In this paper, we describe an extension of an automatic road extraction procedure developed for single SAR images towards multi-aspect SAR images. Extracted information from multi-aspect SAR images is not only redundant and complementary, in some cases even contradictory. Hence, multi-aspect SAR images require a careful selection within the fusion step. In this work, a fusion step based on probability theory is proposed. During fusion each extracted line primitive is assessed by means of Bayesian probability theory. The assessment is based on the attributes of the line primitive (i.e. length, straightness, etc), global context and sensor geometry. The fusion and its integration into the road extraction system are tested in a sub-urban SAR scene.
AB - In this paper, we describe an extension of an automatic road extraction procedure developed for single SAR images towards multi-aspect SAR images. Extracted information from multi-aspect SAR images is not only redundant and complementary, in some cases even contradictory. Hence, multi-aspect SAR images require a careful selection within the fusion step. In this work, a fusion step based on probability theory is proposed. During fusion each extracted line primitive is assessed by means of Bayesian probability theory. The assessment is based on the attributes of the line primitive (i.e. length, straightness, etc), global context and sensor geometry. The fusion and its integration into the road extraction system are tested in a sub-urban SAR scene.
KW - Fusion
KW - Road extraction
KW - SAR data
UR - http://www.scopus.com/inward/record.url?scp=67649793218&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2008.4779759
DO - 10.1109/IGARSS.2008.4779759
M3 - Conference contribution
AN - SCOPUS:67649793218
SN - 9781424428083
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - IV467-IV470
BT - 2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings
T2 - 2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings
Y2 - 6 July 2008 through 11 July 2008
ER -