SAR (synthetic aperture radar) image segmentation method based on total-variation spectral clustering

Abstract

The invention discloses an SAR (synthetic aperture radar) image segmentation method based on total-variation spectral clustering, and mainly solves the problems of low accuracy and poor regional consistency of the existing spectral clustering method when applied to SAR image segmentation. The method comprises the following steps of: (1) performing total-variation image decomposition on the input SAR image; (2) extracting the gray characteristic G of the structure part of the SAR image; (3) extracting the wavelet characteristic T of the texture part of the SAR image; (4) calculating the similarity Wij between any two points i and j in a total sample set S by use of the gray characteristic G and the wavelet characteristic T; (5) approaching to the characteristic vector space after the spectral sampling of the total sample set S through the sampling sample set Sm according to an Nystrom approaching method by use of the sampling sample set Sm selected from the total sample set S, and taking the characteristic vectors corresponding to the first k maximum characteristic values as a dimension-reducing characteristic matrix Y; and (6) performing k-means clustering on the dimension-reducing characteristic vector matrix Y, and outputting the final segmentation result of the SAR image. The method disclosed by the invention has the advantages of high segmentation result accuracy and good regional consistency, and can be applied to the SAR image target detection and target segmentation and recognition.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to SAR image segmentation, and can be used for SAR image target detection, target segmentation and recognition. Background technique [0002] Synthetic Aperture Radar (SAR) imaging technology actively emits and receives electromagnetic waves, and forms images according to the reflection and scattering characteristics of objects. Because it overcomes the shortcomings of traditional imaging technology that must rely on certain lighting conditions, it has all-weather, all-weather, high-resolution, detection and reconnaissance capabilities that can effectively identify camouflage and penetrate cover objects, so the interpretation of SAR images is becoming more and more important. It has received more and more attention and attention from the fields of national defense and civilian use. As one of the key links in SAR image interpretation, SAR image segmentation is becoming more and m...

AI Technical Summary

Problems solved by technology

SAR images not only contain rich texture information, but also have good structural information. However, when the current spectral clustering algorithm is used for SAR image segmentation, it only uses its rich texture information, but rarely considers its good structural information

Therefore, this makes the current spectral clustering algorithm unable to obtain a segmentation result with good regional consistency and high accuracy when it is used for SAR image segmentation

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