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利用星载全极化C波段和X波段合成孔径雷达对海上平台源污染进行监测。

Offshore platform sourced pollution monitoring using space-borne fully polarimetric C and X band synthetic aperture radar.

作者信息

Singha Suman, Ressel Rudolf

机构信息

Maritime Safety and Security Lab, Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Henrich Focke Str. 4, 28199 Bremen, Germany.

出版信息

Mar Pollut Bull. 2016 Nov 15;112(1-2):327-340. doi: 10.1016/j.marpolbul.2016.07.044. Epub 2016 Aug 13.

Abstract

Use of polarimetric SAR data for offshore pollution monitoring is relatively new and shows great potential for operational offshore platform monitoring. This paper describes the development of an automated oil spill detection chain for operational purposes based on C-band (RADARSAT-2) and X-band (TerraSAR-X) fully polarimetric images, wherein we use polarimetric features to characterize oil spills and look-alikes. Numbers of near coincident TerraSAR-X and RADARSAT-2 images have been acquired over offshore platforms. Ten polarimetric feature parameters were extracted from different types of oil and 'look-alike' spots and divided into training and validation dataset. Extracted features were then used to develop a pixel based Artificial Neural Network classifier. Mutual information contents among extracted features were assessed and feature parameters were ranked according to their ability to discriminate between oil spill and look-alike spots. Polarimetric features such as Scattering Diversity, Surface Scattering Fraction and Span proved to be most suitable for operational services.

摘要

将极化合成孔径雷达(SAR)数据用于近海污染监测相对较新,并且在海上作业平台监测方面显示出巨大潜力。本文描述了基于C波段(RADARSAT - 2)和X波段(TerraSAR - X)全极化图像开发用于实际作业目的的自动溢油检测链,其中我们使用极化特征来表征溢油和类似物。已经获取了在近海平台上空几乎同时的TerraSAR - X和RADARSAT - 2图像。从不同类型的油和“类似物”斑点中提取了十个极化特征参数,并将其分为训练和验证数据集。然后使用提取的特征来开发基于像素的人工神经网络分类器。评估了提取特征之间的互信息内容,并根据其区分溢油和类似物斑点的能力对特征参数进行了排序。诸如散射多样性、表面散射分数和跨度等极化特征被证明最适合实际作业服务。

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