Polarimetric SAR image classification method based on semi-supervised SVM and mean shift

Abstract

The invention discloses a polarimetric SAR image classification method based on a semi-supervised SVM and mean shift. The polarimetric SAR image classification method comprises the following steps of respectively establishing a polarimetric SAR image classification training set and an image classification testing set; obtaining a polarimetric SAR image classification result by using an S4VMs algorithm; selecting a sample set, with a high degree of confidence, of the S4VMs classification result; modifying the S4VMs classification result by using a mean shift result, and updating the sample set; updating the training set, the testing set and a classification model; classifying polarimetric SAR images by using the classification model. According to the polarimetric SAR image classification method, threshold value soft division is adopted, and self-adaptivity of the algorithm is improved; the sample set is modified through the mean shift result, image information is completed, the problem that manual marking is difficult is solved, the better classification result is obtained, and the polarimetric SAR image classification method can be used for target detection, identification and classification.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a polarization SAR image classification method based on semi-supervised SVM and MeanShift. Background technique [0002] With the rapid development of electronic technology, especially large-scale integrated circuit technology, synthetic aperture radar (SAR) is developing in the direction of multi-resolution, multi-band, multi-polarization, and multi-working mode, and is committed to providing more abundant target scattering information. Polarimetric SAR (Polarimetric SAR) is a synthetic aperture radar capable of measuring all polarimetric targets, and can describe the target more comprehensively. Its data contains more abundant target scattering information, so polarimetric SAR has very prominent advantages in target detection and recognition, classification and parameter inversion, and has attracted extensive attention from scholars from all over the world since its a...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to address the deficiencies of the above existing methods, to propose a polarimetric SAR image classification method based on semi-supervised SVM (Support Vector Machine, SupportVectorMachine) and MeanShift (mean shift), which improves the adaptability of the algorithm, and Reduce the need for manual marking, effectively combine the target scattering information of image features with image information, and improve the classification effect

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