苏晓洁, 刘秀清. 基于SAR极化特征的SVM道路提取方法研究[J]. 微电子学与计算机, 2022, 39(8): 47-54. DOI: 10.19304/J.ISSN1000-7180.2021.1368
引用本文: 苏晓洁, 刘秀清. 基于SAR极化特征的SVM道路提取方法研究[J]. 微电子学与计算机, 2022, 39(8): 47-54. DOI: 10.19304/J.ISSN1000-7180.2021.1368
SU Xiaojie, LIU Xiuqing. Road extraction based on SAR polarization characteristics and SVM[J]. Microelectronics & Computer, 2022, 39(8): 47-54. DOI: 10.19304/J.ISSN1000-7180.2021.1368
Citation: SU Xiaojie, LIU Xiuqing. Road extraction based on SAR polarization characteristics and SVM[J]. Microelectronics & Computer, 2022, 39(8): 47-54. DOI: 10.19304/J.ISSN1000-7180.2021.1368

基于SAR极化特征的SVM道路提取方法研究

Road extraction based on SAR polarization characteristics and SVM

  • 摘要: SAR图像中道路提取在路网规划建设、灾害监测等领域具有重要的应用价值.传统SAR图像道路提取方法多是基于SAR图像的幅值特性进行提取,缺少对极化特性的解译.此外极化分解方法多应用于水体提取、地物分类、建筑物提取等,较少应用于道路提取.针对现有的道路提取方法数据质量要求高、全极化道路提取研究较少、全极化数据源相干斑噪声影响大的问题,本文首先对全极化数据进行多视处理、滤波去噪预处理,并通过极化分解方法获取20维极化特征散射分量.其次,从散射机理的角度出发,构建鉴别道路信息的最优极化特征矢量.最后,通过SVM分类器得到初步道路提取结果,并通过数学形态法提取道路数据.实验结果表明,该方法达到了98.4%的Acc和65.3%的Iou,具有提取精度高、应用范围广的优点,充分利用高分辨率SAR数据的极化信息,可有效应用于SAR图像的道路提取方法研究中.此外,区分于将光学道路提取的方法直接套用到SAR图像道路提取研究,本文探索了极化特征在SAR图像道路提取中的应用表现,为SAR图像道路提取研究提出新模式新思路.

     

    Abstract: Road extraction from SAR images has important application value in road network planning and construction, disaster monitoring and other fields. The traditional SAR image road extraction method is mainly based on the amplitude characteristics of SAR image, which lacks the interpretation of polarization characteristics. In addition, the polarization decomposition methods are mostly used in water extraction, land classification, building extraction, etc., and are less used in road extraction. Aiming at the problems of high data quality requirements of existing road extraction methods, less research on full polarization road extraction and large influence of speckle noise of full polarization data source, this paper first carries out multi view processing and filtering denoising preprocessing on the full polarization data, and obtains 20-dimensional polarization characteristic scattering components through polarization decomposition method. secondly, from the perspective of scattering mechanism, the optimal polarization feature vector for identifying road information is constructed. Finally, the preliminary road extraction results are obtained by SVM classifier, and road data is extracted by mathematical morphology method. Experimental results show that the proposed method achieves 98.4% Acc and 65.3% Iou, which can be effectively applied to the research of road extraction in SAR images.proposed which making full use of the polarizatio information of high-resolution SAR data, has the advantages of high extraction accuracy and wide application range, it can be effectively applied to the research of road extraction method of SAR image. In addition, different from applying the method of optical road extraction directly to the research of SAR image road extraction, this paper explores the application of polarization features in SAR image road extraction, and puts forward new modes and ideas for the research of SAR image road extraction.

     

/

返回文章
返回