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Dynamically adapted ship parameter estimation using TerraSAR-X images
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AI
DOI：10.1080/01431161.2015.1071898.png)
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摘要
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This article describes how the estimation of ship parameters from ship signatures on TerraSAR-X images can be adapted dynamically using combinatorial optimization and regression analysis. Research in the field of ship detection commonly addresses the improvement of processors with regard to accuracy performance of detection and parameter estimation. While most research implies beneficial improvements to the processors, the different techniques are rarely compared or combined. In this article the Monte Carlo combinatorial optimization (cross-entropy method) is used to evaluate the performance of improvements to parameter estimation and performance of combinations of these improvements. Then multiple linear regression analysis is applied to increase the accuracy of parameter estimation further. The underlying data set consists of TerraSAR-X Stripmap, ScanSAR, and ScanSAR Wide Multi Look Ground Range detected (MGD) images acquired over the North Sea and Baltic Sea with horizontal transmit, horizontal receive or vertical transmit, vertical receive polarization. Validation data are provided by the Automatic Identification System (AIS). The optimization algorithm assesses optimal parameter settings and appropriate combinations of techniques dedicated to this data set. The resulting processor provides a significantly higher accuracy of ship parameter estimation than the initial processor.
Keyword:
SAR IMAGES
SYSTEM
TARGET
SEA
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期刊
IF:
2.6
论文数:
1.2W
被引数:
2.7W
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