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

Jian Sun,Changlong Guan,Qingjun Zhang,Xinzhe Yuan,Alexander Babanin,Xin Wang

doi:10.3390/rs10111843

Abstract

Modeling the statistical distribution of synthetic aperture radar (SAR) images is essential for sea target detection, which is an important aspect of marine SAR applications. The main goal of this study is to determine the effects of sea states and surface wave texture characteristics on the statistical distributions of sea SAR images. A statistical analysis of the Envisat Advanced Synthetic Aperture Radar (ASAR) wave mode images (imagettes), covering a variety of sea states and wave conditions, was carried out to investigate the suitability of the statistical distributions often used in the literature for sea states parameters. The results revealed the variation in the distribution parameters in terms of their azimuthal cutoff wavelength (ACW) and the peak-to-background ratio (PBR) of the SAR image intensity spectra. The shape parameters of Gamma and Weibull distribution are sensitive and monotonously decreasing with respect to PBR, while the scale parameter is sensitive to ACW. The K distribution was shown to perform well, with both high and stable accuracy. The results of this paper provide a parameterized scheme for sea state classifications and can potentially be used for choosing the most suitable distribution model according to sea state when performing sea target detection.

Highlights

Synthetic aperture radar (SAR) is an active microwave sensor that has proven to be one of the most effective systems for acquiring high resolution observations of the ocean surface in all weather conditions
A statistical model of the ocean SAR images across a wide range of sea states is essential for sea target detection and sea classification
Taking advantage of the Envisat Advanced Synthetic Aperture Radar (ASAR) wave mode level-1 images and validating the results using the level-2 wave products, the present study investigated the dependence of sea SAR image distributions on surface wave characteristics

Summary

Introduction

Synthetic aperture radar (SAR) is an active microwave sensor that has proven to be one of the most effective systems for acquiring high resolution observations of the ocean surface in all weather conditions. The interpretation and investigation of the features of SAR image pixels, using normalized radar cross sections (NRCSs) provides a large amount of quantitative and qualitative information for a variety of applications in oceanic remote sensing [1]. Accurate modeling of the statistical distribution of SAR images over a background sea surface plays a key role in target detection since precise knowledge of sea clutter distributions is required for the construction of a target detector [2,3,4,5,6]. The statistical properties of the backscattered radar cross section (RCS) of a sea target are distinguishable from those of the background sea clutter, and information about the objects can be extracted when an appropriate threshold is known.

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Conclusion