SAR (synthetic aperture radar) image segmentation method based on GMM (gaussian mixture model) parameter transferring and clustering

Abstract

The invention discloses a (synthetic aperture radar) SAR image segmentation technical method based on GMM (gaussian mixture model) parameter transferring and clustering. According to the method, the problems of unstable segmentation results, and unsatisfactory precision and area consistency in the prior art are mainly solved. The SAR image segmentation technical method comprises the following implementation steps of: 1) inputting an image, and determining segmentation class numbers; 2) extracting features; 3) setting initial parameters; 4) clustering a sample for seven times to acquire seven groups of clustering results; 5) solving clustering consistency values; 6) dividing the sample into a source domain and a target domain according to the clustering consistency values; 7) estimating the parameters of the source domain by using an EM (expectation-maximization) algorithm; 8) searching K neighbor points of the target domain sample in the source domain sample, and solving the clustering consistency values of the points; 9) solving new parameters of the target domain according to the consistency values of the K neighbor points and the parameters of the source domain; and 10) solvinga probability value of each sample of the target domain according to the new parameters, and acquiring a final segmentation result of the image. The SAR image segmentation technical method has the advantages of stable and good segmentation effect and high area consistency, and can be used for detecting and recognizing radar targets.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to SAR image segmentation, and can be used for radar target detection and target recognition. Background technique [0002] Synthetic Aperture Radar (SAR) has all-weather and all-weather detection and reconnaissance capabilities. It uses pulse compression technology to obtain high distance resolution, and uses synthetic aperture principle to improve azimuth resolution, so it has unique advantages in the field of remote sensing compared with real aperture radar. The understanding and interpretation of SAR images belongs to the category of image processing, and also involves many disciplines such as signal processing, pattern recognition and machine learning. Due to the unique role of SAR, the understanding and interpretation of SAR images is receiving more and more attention in the fields of national defense and civilian use. SAR image segmentation, as one of the key links in the...

AI Technical Summary

Problems solved by technology

The commonly used technique in clustering is machine learning, which has achieved remarkable success, but many machine learning methods are based on the assumption that the training data and test data come from the same distribution and the same feature space, so when the data distribution changes , most machine learning methods need to learn from scratch, requiring users to re-collect a large amount of training data to train the model

In the real world, re-collecting data and then labeling it would be very expensive

In addition, the classic machine learning method EM algorithm is widely used because it is simple and easy to implement, but it is also sensitive to the initial value, and it is easy to fall into local optimal defects, resulting in instability and misclassification of image segmentation.

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