Road extraction based on SAR polarization characteristics and SVM

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.