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Proceedings Paper

Speckle noise reduction in SAR images ship detection
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Paper Abstract

At present, there are two types of method to detect ships in SAR images. One is a direct detection type, detecting ships directly. The other is an indirect detection type. That is, it firstly detects ship wakes, and then seeks ships around wakes. The two types all effect by speckle noise. In order to improve the accuracy of ship detection and get accurate ship and ship wakes parameters, such as ship length, ship width, ship area, the angle of ship wakes and ship outline from SAR images, it is extremely necessary to remove speckle noise in SAR images before data used in various SAR images ship detection. The use of speckle noise reduction filter depends on the specification for a particular application. Some common filters are widely used in speckle noise reduction, such as the mean filter, the median filter, the lee filter, the enhanced lee filter, the Kuan filter, the frost filter, the enhanced frost filter and gamma filter, but these filters represent some disadvantages in SAR image ship detection because of the various types of ship. Therefore, a mathematical function known as the wavelet transform and multi-resolution analysis were used to localize an SAR ocean image into different frequency components or useful subbands, and effectively reduce the speckle in the subbands according to the local statistics within the bands. Finally, the analysis of the statistical results are presented, which demonstrates the advantages and disadvantages of using wavelet shrinkage techniques over standard speckle filters.

Paper Details

Date Published: 19 October 2012
PDF: 8 pages
Proc. SPIE 8532, Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2012, 853210 (19 October 2012); doi: 10.1117/12.974375
Show Author Affiliations
Ji Yuan, Institute of Remote Sensing Applications (China)
Bin Wu, Institute of Remote Sensing Applications (China)
Yuan Yuan, Institute of Remote Sensing Applications (China)
Qingqing Huang, Institute of Remote Sensing Applications (China)
Jingbo Chen, Institute of Remote Sensing Applications (China)
Lin Ren, Second Institute of Oceanography, SOA (China)

Published in SPIE Proceedings Vol. 8532:
Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2012
Charles R. Bostater Jr.; Stelios P. Mertikas; Xavier Neyt; Caroline Nichol; Dave Cowley; Jean-Paul Bruyant, Editor(s)

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