SAR (Synthetic Aperture Radar) image segmentation method based on parallel sparse spectral clustering

Abstract

The invention discloses an SAR (Synthetic Aperture Radar) image segmentation method based on parallel sparse spectral clustering, relating to the technical field of image processing and mainly solving the problem of limitation of segmentation application of large-scale SAR images in the traditional spectral clustering technology. The SAR image segmentation method comprises the steps of: 1, extracting features of an SAR image to be segmented; 2, configuring an MATLAB (matrix laboratory) parallel computing environment; 3, allocating tasks all to processor nodes and computing partitioned sparse similar matrixes; 4, collecting computing results by a parallel task dispatcher and merging into an integral sparse similar matrix; 5, resolving a Laplacian matrix and carrying out feature decomposition; 6, carrying out K-means clustering on a feature vector matrix subjected to normalization; and 7, outputting a segmentation result of the SAR image. The invention can effectively overcome the bottleneck problem in computation and storage space of the traditional spectral clustering technology, has remarkable segmentation effect on large-scale SAR images, and is suitable for SAR image target detection and target identification.

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. SAR image segmentation, as one of the key links in SAR image processing, is receiving more and more attention in the fields of national defense and civilian use. Existing SAR image segmentation methods can be roughly divided...

AI Technical Summary

Problems solved by technology

However, when the data size n is relatively large, the spectral clustering algorithm needs to calculate the similarity matrix of size n×n, and calculate the eigendecomposition problem of the corresponding Laplacian matrix. The time complexity and space complexity of the calculation are respectively is O(n 3 ) and O(n 2 ), cannot effectively segment SAR images containing large-scale data

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