Statistical Analysis of SAR Sea Clutter for Classification Purposes

Abstract

Statistical 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.

The Dependence of Sea SAR Image Distribution Parameters on Surface Wave Characteristics

“…For the Gamma distribution, the scale parameter monotonously increases with ACW while the shape parameter decreases with PBR. This result is consistent with the result of [32]. Meanwhile, the increase in PBR results in the expanding range of the scale parameter for the Gamma and Weibull distributions.…”

“…The current study statistically investigated the dependence of the PDF parameters on the properties of sea states, including the ACW and PBR. By analyzing a limited number of TerraSAR-X scenes, [32] showed that the distribution parameters could be used for sea state classification. Their results are confirmed by the further detailed sensitivity analysis of the present study using SAR images with a variety of sea states.…”

“…The impact of sea states on sea surface statistical distribution was recently considered in a study by Martin-de-Nicolas et al [32]. Their work demonstrated that the statistical parameters of the clutter distribution are variable and dependent on the sea state when performing a sea surface analogous classification to that of the land texture classification.…”

A waterborne salient ship detection method on SAR imagery

“…Factors such as speckle noise, bathymetry or wind changed marine discontinuities, oil spills, ship wakes and harbor areas may cause the non-uniform ocean surface [1,2]. Whereas, various sea clutter describing approaches have been available in the literature, it remains an open and challenging problem which greatly restricts the performance of constant false alarm rate (CFAR) based ship detection algorithms [3,4].…”

The SUMO Ship Detector Algorithm for Satellite Radar Images

“…Many other parametric distributions have been proposed to model radar sea clutter; see the reviews [47,48]. The ones mostly used are Gaussian [12], log-normal and Weibull [44,49,50].…”