SAR (Synthetic Aperture Radar) image segmentation method based on sampling learning

Abstract

The invention discloses an SAR (Synthetic Aperture Radar) image segmentation method based on sampling learning, mainly solving the problems of huge computation and slow segmentation speed of conventional algorithm. The SAR image segmentation method comprises the following steps: (1) inputting an image to be segmented, and extracting the characteristics; (2) randomly sampling a data set for M times; (3) respectively clustering the data sets of the samples acquired for M times through the spectral clustering algorithm; (4) combining the data of the same type after the clustering implemented for M times, wherein a relative new data set is generated by the combined data of the same type, and training a dictionary for the new data set through KSVD (Singular Value Decomposition) algorithm; (5) calculating sparse codes of the testing samples in the dictionary; (6) calculating the reconstruction error of the testing samples in the dictionary; and (7) determining the labels of the testing samples based on the reconstruction error, so as to obtain the final segmentation result. The SAR image segmentation method based on sampling learning has the advantage of being quick and accurate in segmentation, and can be further applied to target recognition and classification of the SAR image.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to image segmentation, and is used for SAR image target recognition and classification. Background technique [0002] With the development of science and technology, people increasingly express various information in the form of images. Image segmentation has also become a research hotspot. [0003] Synthetic aperture radar SAR is not affected by factors such as climate, day and night, and has the advantage of all-weather imaging. It uses the principle of synthetic aperture to improve azimuth resolution, and uses pulse compression technology to obtain high distance resolution, so it has great advantages in the field of remote sensing compared with real aperture radar. Due to the unique role of SAR, the understanding and interpretation of SAR images is receiving more and more attention in the fields of national defense and civilian use. [0004] At present, many mature clusteri...

AI Technical Summary

Problems solved by technology

However, clustering-based SAR image segmentation methods such as spectral clustering algorithms need to be calculated in units of pixels, and the dimension of the square matrix used in it is the number of all pixels in the image, which leads to the data that needs to be processed The dimension is very large, which makes the segmentation time very long, and it is difficult to implement on a general computer, which also limits the application of the algorithm.

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