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Dark spot detection for characterization of marine surface slicks using PolSAR remote sensing

机译:使用PolSAR遥感技术进行黑点检测以表征海洋表面浮油

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Oceans are considered as the important source for oil reserves and continuous activities like oil extraction and transportation may sometimes cause the accidental release of oil into the sea surface which causes a major threat to the marine ecosystems, economy and human life. The prime focus of this study is to explore the potential of the fully polarimetric SAR data and analyze the different scattering mechanisms for the oil spilled regions. In this study the fully polarized and orthorectified, L band data of UAVSAR airborne sensor is used which is captured on June 22nd 2010, during which the Deepwater Horizon oil spill occurred in the Gulf of Mexico. For the detection of oil spill different decomposition techniques such as Freeman, Yamaguchi and H/A/α are studied and classified using Wishart classification. Freeman and Yamaguchi decomposition helped in understanding the type of scattering mechanism taking place in slick covered regions, sea surface and in the presence of ships/rig. A set of polarimetric parameters such as magnitude of correlation coefficient, cross product of co polarized channels, anisotropy, alpha ,entropy and the intensity of the coherency matrix are studied which helped in distinguishing the oil spills, sea surface and the look-alikes. The Wishart classification result of Freeman and Yamaguchi decompositions showed more reliable results in comparison to the K-means classification results obtained through segmentation of combined H/A/α decomposition. The entropy, anisotropy and magnitude of correlation coefficient are dependent on the angle of incidence. At low incidence angle the entropy value of oil spills are similar to that of the sea surface whereas the magnitude of correlation coefficient which is a function of dielectric constant, increases for oil spills at low incidence angle. The polarimetric parameter, intensity of the coherency matrix utilizes the whole coherency matrix by calculating its determinant and proven to provide good discrimination between the oil spills and the sea surface.
机译:海洋被认为是石油储备的重要来源,持续的活动(例如石油开采和运输)有时可能会导致石油意外释放到海面,从而对海洋生态系统,经济和人类生命造成重大威胁。这项研究的主要重点是探索全极化SAR数据的潜力,并分析漏油区域的不同散射机制。在这项研究中,使用了UAVSAR机载传感器的完全极化和正射L波段数据,该数据是在2010年6月22日捕获的,其间在墨西哥湾发生了Deepwater Horizo​​n漏油事件。为了检测漏油,研究了不同的分解技术,例如Freeman,Yamaguchi和H / A /α,并使用Wishart分类对其进行了分类。 Freeman和Yamaguchi分解有助于了解在光滑覆盖区域,海面以及在船舶/钻机在场的情况下发生的散射机制的类型。研究了一组极化参数,如相关系数的大小,同极化通道的叉积,各向异性,α,熵和相干矩阵的强度,这有助于区分溢油,海面和外观。 Freeman和Yamaguchi分解的Wishart分类结果显示出比通过组合H / A /α分解得到的K-均值分类结果更可靠的结果。熵,各向异性和相关系数的大小取决于入射角。在低入射角下,溢油的熵值类似于海面的熵值,而相关系数的大小(是介电常数的函数)对于低入射角下的溢油会增加。相干矩阵的极化参数强度通过计算其行列式来利用整个相干矩阵,并被证明可以很好地区分溢油和海面。

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