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

• Main Authors: Jian Sun, Xin Wang, Xinzhe Yuan, Qingjun Zhang, Changlong Guan, Alexander V. Babanin
• Format: Article
• Language: English
• Published: MDPI AG 2018-11-01
• Series: Remote Sensing
• Subjects: statistical models; wind waves; swell; Synthetic Aperture Radar; classification
• Online Access: https://www.mdpi.com/2072-4292/10/11/1843

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.