利用SAR影像区域分割方法提取海洋暗斑特征

在SAR强度影像中,包括海洋溢油在内的许多海洋现象呈现为暗斑。为从诸多暗斑中辨识海洋溢油,需要在SAR影像中提取暗斑的几何和统计分布特征,以此作为进一步分类（辨识）海洋溢油的依据,将基于几何划分技术的区域分割方法应用于SAR影像暗斑特征提取。首先建立高分辨率SAR影像暗斑或然率模型,然后利用最大化期望值和M-H算法实现其几何及统计分布特征参数提取。实验结果表明,该方法不仅可以精准提取暗斑的几何形状,同时还能有效估计其统计分布参数。

Feature Extraction of Dark Spot Based on the SAR Image Segmentation

Marine oil spills from operational discharges and ship accidents always have calamitous impacts on the marine environment and ecosystems, even with small oil coverage volumes. Remote sensing solutions using space-borne or airborne sensors are playing an increasingly important role in monitoring, tracking and measuring oil spills and are receiving much more attention from governments and organizations around the world. Compared to airborne sensors, satellite sensors, with their large extent observation, timely data available and all weather operation, have been proven to be more suitable for monitoring oil spills in marine environments, whilst the latter can be easily used to identify polluters and oil spill types but are of limited use due to costs and weather conditions. Currently, the commonly used satellite SAR sensors for this purpose include RADARSAT-1/2, ENVISAT, ERS-1/2, and so on. The detectability of oil spills by SAR images is based on the fact that oil slicks dampen the Bragg waves on the ocean surface and reduce the radar backscatter coefficient. Unfortunately, many other physical phenomena, for example, low-wind areas, wind-shadow areas near coasts, rain cells, currents, upswelling zones, biogenic films, internal waves, and oceanic or atmospheric fronts, can also generate dark areas, known as look-alikes, in SAR intensity images. Another factor which influences the backscatter level and the visibility of oil slicks on the sea surface is the wind level. Oil slicks are visible only for a limited range of wind speeds. Generally speaking, SAR based oil spill recognition includes three stages: dark spot detection, dark spot feature extraction and oil spill classification. The work in this article focuses on the feature extraction of detected dark spots. The task at this stage involves defining and acquiring the features existing in SAR intensity images, which can be efficiently used in the classification stage to distinguish oil spills from look-alikes. Commonly defined features for this purpose include the geometry and shape of the dark spot area, textures, contrast between dark spots and their surroundings, and dark spot contextual information. To this end, this article presents regional image segmentation for dark spot feature extraction from SAR intensity image, which is completed by Metropolis-Hastings (M-H) and expectation maximum estimate algorithm. To segment a SAR intensity image, it is reasonable to approximate the homogenous regions in an SAR intensity image by Voronoi polygons. The number of Voronoi polygons is assumed unknown. The marine background and dark spot regions, in which the pixel intensities are assumed to follow independent and identical Gaussian distributions, consist of some partitioned sub-regions. On the basis of the image domain partition, the SAR intensity image is statistically modeled by two Gaussian distributions. And then the SAR intensity image segmentation is performed by the M-H and expectation maximum estimate algorithm for extracting the geometries and statistical parameters of dark spots. In order to verify the validness of the proposed method, testing is carried out on simulated and real SAR intensity images. The results from all test images are qualitatively and quantitatively evaluated and show that the proposed algorithm works well on dark spot feature extraction.