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

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.