Paper
28 October 2006 Automated targets detection based on level set evolution using radar and optical imagery
Yun Yang, Hongchao Ma, Yan Song
Author Affiliations +
Proceedings Volume 6419, Geoinformatics 2006: Remotely Sensed Data and Information; 641913 (2006) https://doi.org/10.1117/12.713013
Event: Geoinformatics 2006: GNSS and Integrated Geospatial Applications, 2006, Wuhan, China
Abstract
Level set evolution theory is introduced to bridge or dam detection above river in order to improve performance in case of very low contrast and faint targets feature in optical or radar imagery. Aiming at shortages like boundary leak, weak robust to noises existing in classical level set methods, and sub- or over- segmentation, irregular boundary with gap existing in traditional segmentation, an adaptive narrow band level set evolution model based on Chan-Vese model is presented to excellently extract river regions from radar imagery with faint edge and unwelcome effects, while greatly accelerate the curve evolution process. Furthermore, we propose a novel algorithm based on Narrow Band Level Set(NBLS) for detecting and simultaneously distinguishing bridge and dam. The algorithm is efficient, avoiding the disadvantages that medial-axis search methods are subjected to noises and are hard to process river branch with complex shape. Finally, feature-weighted decision rule is adopted to combine the detection results from the two binary classifiers form radar and optical imagery, in order to make use of complementary feature from different classifiers and to achieve higher accuracy of targets detection than single classifier. Experimental results demonstrate that our scheme proposed in the paper outperform some others, with the advantages of time-effectiveness and robust to noises.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yun Yang, Hongchao Ma, and Yan Song "Automated targets detection based on level set evolution using radar and optical imagery", Proc. SPIE 6419, Geoinformatics 2006: Remotely Sensed Data and Information, 641913 (28 October 2006); https://doi.org/10.1117/12.713013
Lens.org Logo
CITATIONS
Cited by 1 scholarly publication.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Target detection

Bridges

Image segmentation

Radar

Detection and tracking algorithms

Binary data

Earth observing sensors

RELATED CONTENT


Back to Top