Paper
28 October 2006 Illicit vessel identification in inland waters using SAR image
Fengli Zhang, Bingfang Wu, Lei Zhang, Huiping Huang, Yichen Tian
Author Affiliations +
Proceedings Volume 6419, Geoinformatics 2006: Remotely Sensed Data and Information; 64190S (2006) https://doi.org/10.1117/12.712973
Event: Geoinformatics 2006: GNSS and Integrated Geospatial Applications, 2006, Wuhan, China
Abstract
Synthetic Aperture Radar remote sensing has been effectively used in water compliance and enforcement, especially in ship detection, but it is still very difficult to classify or identify vessels in inland water only using existing SAR image. Nevertheless some experience knowledge can help, for example waterway channel is of great significance for water traffic management and illegal activity monitoring. It can be used for judging a vessel complying with traffic rules or not, and also can be used to indicate illicit fishing vessels which are usually far away from navigable waterway channel. For illicit vessel identification speed and efficiency are very important, so it will be significant if we can extract waterway channel directly from SAR images and use it to identify illicit vessels. The paper first introduces the modified two-parameter CFAR algorithm used to detect ship targets in inland waters, and then uses principal curves and neural networks to extract waterway channel. Through comparing the detection results and the extracted waterway channel those vessels not complying with water traffic rules or potential illicit fishing vessels can be easily identified.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Fengli Zhang, Bingfang Wu, Lei Zhang, Huiping Huang, and Yichen Tian "Illicit vessel identification in inland waters using SAR image", Proc. SPIE 6419, Geoinformatics 2006: Remotely Sensed Data and Information, 64190S (28 October 2006); https://doi.org/10.1117/12.712973
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KEYWORDS
Target detection

Synthetic aperture radar

Detection and tracking algorithms

Neural networks

Sensors

Water

Neurons

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