Dynamically adapted ship parameter estimation on TerraSAR-X images

Kurzfassung

This article describes how the estimation of ship parameters from ship signatures on TerraSAR-X images can be improved 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 of parameter estimation. While most research implies beneficial improvements to the processors, the different techniques are rarely compared or combined. In this article Monte-Carlo combinatorial optimization (cross-entropy method) is used to evaluate the performance of improvements to parameter estimation and the 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 North Sea and Baltic Sea with HH or VV polarization. Validation data is provided by the Automatic Identification System (AIS). The optimization algorithm assesses optimal parameter settings and appropriate combinations of techniques dedicated to this dataset. The resulting processor provides a significantly higher accuracy of ship parameter estimation.