Automated targets detection based on level set evolution using radar and optical imagery

Yun Yang; Hongchao Ma; Yan Song

doi:10.1117/12.713013

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28 October 2006 Automated targets detection based on level set evolution using radar and optical imagery

Yun Yang; Hongchao Ma; Yan Song

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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.

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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

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