SAR image unsupervised change detection method based on static wavelet transform extraction

Abstract

The invention discloses an SAR image unsupervised change detection method based on static wavelet transform extraction. The SAR image unsupervised change detection method comprises the following implementation steps: firstly, a preprocessing stage; carrying out filtering smoothing processing on the two SAR images by using an Lee filter; and secondly, a difference chart generation stage, generating a difference chart by using a logarithmic ratio operator; and finally, a difference chart analysis stage, firstly, using SWT2 (db2) (two-dimensional static wavelet transform, and db2 is a wavelet basis function) to decompose a difference image, and obtaining an approximate image, a horizontal detail image, a vertical detail image and a diagonal detail image, respectively decomposing the four groups of images by using EM_GGM, finally selecting second-order neighborhood window probabilities to form feature vectors, and obtaining a result through K-means.

Description

technical field [0001] The invention belongs to the technical field of image change detection, and relates to an unsupervised change detection method for SAR images based on static wavelet transform extraction. Background technique [0002] SAR is an active side-looking radar system, and the imaging geometry belongs to the oblique-range projection type. Therefore, SAR images are quite different from optical images in terms of imaging mechanism, geometric features, and radiation features. SAR uses the principle of synthetic aperture to realize high-resolution microwave imaging, and has various characteristics such as all-day, all-weather, high-resolution, and large width. Therefore, the SAR system has unique advantages in the application of disaster monitoring, environmental monitoring, ocean monitoring, resource exploration, crop yield estimation, surveying and mapping, and military applications, and can play a role that other remote sensing methods are difficult to play. ...

AI Technical Summary

Problems solved by technology

The nature of the logarithmic operation itself can reduce the large difference caused by the ratio operation, so it can further reduce the influence of wild points in the unchanged background part. It is more effective when the changed area is smaller than the unchanged area, but because the logarithm The operation shrinkage is strong, so the pixel values ​​​​in the edge area are easily blurred

2. The mean ratio (Mean-ratio, MR) operator operation, the object of comparison is no longer the corresponding isolated pixel point, but the mean value of the neighborhood where the pixel point is located, which has a certain degree of inhibition on the wild points that appear alone , but due to the lack of scaling transformation, if the noise does not appear in the form of points but in the form of patches, it is difficult to effectively suppress its influence

This method is simple and easy to implement, and is suitable for parallel processing, and the speed is fast; however, it contains artificial parameters and requires multiple tests to obtain the optimal parameter value, and it is not easy to automatically select according to the nature of the image itself.

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