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Detection of vehicles in shadow areas using combined hyperspectral and LIDAR data
Signal and Image Centre, Dept. of Electrical Engineering (SIC-RMA), Brussels, Belgium.
FOI Swedish Defence Research Agency, Linköping, Sweden.
Dept. of Mathematics, Royal Military Academy, Brussels, Belgium.
FOI Swedish Defence Research Agency, Linköping, Sweden.ORCID iD: 0000-0002-6763-5487
2011 (English)In: 2011 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IEEE , 2011, 4427-4430 p.Conference paper (Refereed)
Abstract [en]

In an effort to overcome the limitations of small target detection in complex urban scene, complementary data sets are combined to provide additional insight about a particular scene. This paper presents a method based on shape/spectral integration (SSI) decision level fusion algorithm to improve the detection of vehicles in semi and deep shadow areas. A four steps process combines high resolution LIDAR and hyperspectral data to classify shadow areas, segment vehicles in LIDAR data, detect spectral anomalies and improves vehicle detection. The SSI decision level fusion algorithm was shown to outperform detection using a single data set and the utility of shape information was shown to be a way to enhance spectral target detection in complex urban scenes.  

Place, publisher, year, edition, pages
IEEE , 2011. 4427-4430 p.
Series
, IEEE International Geoscience and Remote Sensing Symposium proceedings, ISSN 2153-6996
Keyword [en]
Target detection, anomaly detection, 3D LIDAR, hyperspectral, fusion
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-120511DOI: 10.1109/IGARSS.2011.6050214ISBN: 978-1-4577-1003-2OAI: oai:DiVA.org:liu-120511DiVA: diva2:845467
Conference
IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Vancouver, BC, Canada, 24-29 July 2011
Available from: 2015-08-12 Created: 2015-08-12 Last updated: 2015-09-22Bibliographically approved

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Ahlberg, Jörgen
Computer Vision and Robotics (Autonomous Systems)

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ReferencesLink to record
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