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231225s2021 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2021.3089936
|2 doi
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|a pubmed24n1090.xml
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|a (DE-627)NLM327133996
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|a (NLM)34166190
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|a DE-627
|b ger
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Chen, Si-Wei
|e verfasserin
|4 aut
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|a Speckle-Free SAR Image Ship Detection
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|c 2021
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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|a Date Revised 01.07.2021
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Ship detection is one of important applications for synthetic aperture radar (SAR). Speckle effects usually make SAR image understanding difficult and speckle reduction becomes a necessary pre-processing step for majority SAR applications. This work examines different speckle reduction methods on SAR ship detection performances. It is found out that the influences of different speckle filters are significant which can be positive or negative. However, how to select a suitable combination of speckle filters and ship detectors is lack of theoretical basis and is also data-orientated. To overcome this limitation, a speckle-free SAR ship detection approach is proposed. A similar pixel number (SPN) indicator which can effectively identify salient target is derived, during the similar pixel selection procedure with the context covariance matrix (CCM) similarity test. The underlying principle lies in that ship and sea clutter candidates show different properties of homogeneity within a moving window and the SPN indicator can clearly reflect their differences. The sensitivity and efficiency of the SPN indicator is examined and demonstrated. Then, a speckle-free SAR ship detection approach is established based on the SPN indicator. The detection flowchart is also given. Experimental and comparison studies are carried out with three kinds of spaceborne SAR datasets in terms of different polarizations. The proposed method achieves the best SAR ship detection performances with the highest figures of merits (FoM) of 97.14%, 90.32% and 93.75% for the used Radarsat-2, GaoFen-3 and Sentinel-1 datasets, accordingly
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|a Journal Article
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| 700 |
1 |
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|a Cui, Xing-Chao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Xue-Song
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xiao, Shun-Ping
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 30(2021) vom: 24., Seite 5969-5983
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
1 |
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|g volume:30
|g year:2021
|g day:24
|g pages:5969-5983
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|u http://dx.doi.org/10.1109/TIP.2021.3089936
|3 Volltext
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|d 30
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|b 24
|h 5969-5983
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