SAR (Synthetic Aperture Radar) image segmentation method based on Treelets and fuzzy C-means clustering

Abstract

The invention discloses an SAR (Synthetic Aperture Radar) image segmentation method based on Treelets and fuzzy C-means clustering, mainly solving the problem that target identification cannot be manually carried out on a large quantity of images in the prior art. The SAR image segmentation method comprises the steps of: 1, extracting texture features and wavelet features of an input SAR image to be segmented; 2, forming the texture features and the wavelet features into a feature matrix; 3, carrying out Treelets conversion on the feature matrix to obtain a basis matrix; 4, multiplying the feature matrix with a scaling function of the basis matrix to obtain a structural vector; and 5, carrying out fuzzy C-means clustering on the structural vector to obtain a segmentation result of the SAR image. The invention can be used for reducing dimensions of high-dimensional data before clustering by utilizing the Treelets conversion, can effectively inhibit the noise and improve the segmentation speed of the SAR image, and is used for automatic segmentation on the SAR image for carrying out target identification.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to a SAR image segmentation method, in particular to a SAR image segmentation method based on Treelets and fuzzy C-means clustering, which can be used for target recognition. Background technique [0002] As an active radar, Synthetic Aperture Radar (SAR) has the advantages of all-time, all-weather, multi-polarization, and multi-view, and has been widely used in military, remote sensing and other fields. Since the SAR image is formed by microwave echo imaging, the complex ground object background and the resulting scattering coherent speckle noise make the segmentation of SAR image more complex than natural images and medical images. At present, SAR image segmentation methods are mainly divided into two categories: data-driven segmentation methods that do not rely on prior knowledge and model-driven segmentation methods based on prior knowledge. The model-driven SAR image segmentation...

AI Technical Summary

Problems solved by technology

However, Treelets itself is a data-driven analysis method. The multi-scale orthogonal basis obtained after transformation is itself a response to the internal structure of the original data. This method does not make full use of the data-driven characteristics of Treelets itself. Both the determination of the best base of Treelets and the training of the final classifier need to be determined through artificially selected samples. This method of adding artificial assistance to distinguish images is not conducive to the automation of image processing, and cannot perform target recognition on a large number of images. thus reducing its usefulness

Images