Statistical Analysis of SAR Sea Clutter for Classification Purposes
AbstractStatistical analysis of radar clutter has always been one of the topics, where more effort has been put in the last few decades. These studies were usually focused on finding the statistical models that better fitted the clutter distribution; however, the goal of this work is not the modeling of the clutter, but the study of the suitability of the statistical parameters to carry out a sea state classification. In order to achieve this objective and provide some relevance to this study, an important set of maritime and coastal Synthetic Aperture Radar data is considered. Due to the nature of the acquisition of data by SAR sensors, speckle noise is inherent to these data, and a specific study of how this noise affects the clutter distribution is also performed in this work. In pursuit of a sense of wholeness, a thorough study of the most suitable statistical parameters, as well as the most adequate classifier is carried out, achieving excellent results in terms of classification success rates. These concluding results confirm that a sea state classification is not only viable, but also successful using statistical parameters different from those of the best modeling distribution and applying a speckle filter, which allows a better characterization of the parameters used to distinguish between different sea states. View Full-Text
Share & Cite This Article
Martín-de-Nicolás, J.; Jarabo-Amores, M.-P.; Mata-Moya, D.; del-Rey-Maestre, N.; Bárcena-Humanes, J.-L. Statistical Analysis of SAR Sea Clutter for Classification Purposes. Remote Sens. 2014, 6, 9379-9411.
Martín-de-Nicolás J, Jarabo-Amores M-P, Mata-Moya D, del-Rey-Maestre N, Bárcena-Humanes J-L. Statistical Analysis of SAR Sea Clutter for Classification Purposes. Remote Sensing. 2014; 6(10):9379-9411.Chicago/Turabian Style
Martín-de-Nicolás, Jaime; Jarabo-Amores, María-Pilar; Mata-Moya, David; del-Rey-Maestre, Nerea; Bárcena-Humanes, José-Luis. 2014. "Statistical Analysis of SAR Sea Clutter for Classification Purposes." Remote Sens. 6, no. 10: 9379-9411.