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紧缩极化SAR卷积神经网络溢油检测方法

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为研究利用紧缩极化SAR代替全极化SAR进行海洋溢油检测的可行性,以及不同极化参数对溢油检测准确率的影响,本文利用卷积神经网络(CNN)的SAR溢油检测算法,对全极化模式及由全极化构造的紧缩极化SAR数据分别提取极化参数,研究其对于溢油分类准确率的影响;同时对比不同SAR数据预处理步骤对溢油检测精度的影响.研究结果表明,对于预处理步骤,线性拉伸方法能够有效提升溢油检测的准确率;在极化参数选择上,极化参数极化熵H在全极化与紧缩极化模式上都取得最高的分类准确率,分别为0.972和0.978.该研究结果证明了利用紧缩极化SAR代替全极化SAR进行溢油检测的可行性,在溢油检测方面具有较好的应用潜力.
Oil spill detection method of compact polarization SAR based on convolution neural network
To investigate the feasibility of using compact polarimetric synthetic aperture radar (SAR) as an alternative to fully polarimetric SAR for oil spill detection and to determine the impact of different polarization parameters on the accuracy of oil spill detection. To this end,a SAR oil spill detection algorithm based on convolutional neural networks (CNN) is employed. This algorithm extracts polarization parameters from both fully polarimetric and derived compact polarimetric SAR data to study their impact on the classification accuracy of oil spills. Furthermore, the impact of different SAR data preprocessing steps on the accuracy of oil spill detection is evaluated. The results demonstrate that the linear stretching method can effectively enhance the accuracy of oil spill detection. Concerning the selection of polarization parameters,the polarization entropy H achieved the highest classification accuracy in both fully polarimetric and compact polarimetric modes, with a classification accuracy of 0.972 for fully polarimetric and 0.978 for compact polarimetric. This demonstrates the potential of using compact polarimetric SAR for oil spill detection and its promising application prospects.

marine oil spillsynthetic aperture radarcompact polarizationpolarization decompositionconvolutional neural network

罗卿莉、陈志远、刘宇婷、张进、李煜

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天津大学精密测试技术及仪器全国重点实验室,天津300072

北京工业大学,北京100036

海洋溢油 合成孔径雷达 紧缩极化 极化分解 卷积神经网络

天津市轨道交通导航定位及时空大数据技术重点实验室开放课题基金城市轨道交通数字化建设与测评技术国家工程实验室开放课题基金天津市自然科学基金重点项目天津市交通运输科技发展计划天津市交通运输科技发展计划国家自然科学基金国家自然科学基金

TKL2023B102021ZH0421JCZDJC006702022-402020-024160144641801284

2024

10.13474/j.cnki.11-2246.2024.0603

测绘通报
测绘出版社

测绘通报

CSTPCD北大核心
影响因子：1.027
ISSN：0494-0911
年,卷(期)：2024.(6)
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