SAR Image Classification Method Based on Sparse Representation and Gaussian Distribution

Abstract

The invention discloses a SAR image classification method based on sparse representation and Gaussian distribution, the steps of which are: (1) inputting a synthetic aperture radar SAR image; (2) generating a dictionary atom matrix and a sparse coefficient matrix of a training set; (3) calculating The weight matrix and the binary matrix of the sparse coefficient matrix; (4) utilize the Gaussian distribution to generate the transition matrix of the dictionary atomic matrix; (5) update the dictionary atomic matrix; (6) update the weight matrix; (7) update the binary matrix; (8) Obtain the sparse coefficient matrix; (9) Determine whether the error of the dictionary atomic matrix reaches 10 ‑6 , if so, then get the dictionary atom matrix of the trained training set and the trained sparse coefficient matrix, execute step (10), otherwise, execute step (4); (10) obtain the classifier of the training set; (11) pair Classify the test set. The invention adopts the methods of sparse representation and Gaussian modeling to improve classification accuracy.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a SAR (Synthetic Aperture Rader) image classification method based on sparse representation and Gaussian distribution in the technical field of image classification. The invention can be used to identify the target in the synthetic aperture radar SAR image, and can also be used to classify the ground objects in the synthetic aperture radar SAR image. Background technique [0002] Synthetic aperture radar SAR (Synthetic Aperture Radar) is a high-resolution imaging radar, which is widely used in military, agriculture, navigation, geographic surveillance and many other fields due to its unaffected by the environment, strong penetrating power, and high resolution. . Therefore, in the military field, synthetic aperture radar can detect important military targets such as armored vehicles, tanks, and aircraft. In the civilian field, it can also conduct research on the di...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that the classification dictionary matrix used is obtained by solving the sparse representation, and the error between the sample matrix and the dictionary is large after only one calculation, thereby reducing the classification accuracy of the synthetic aperture radar SAR image

The disadvantage of this method is that the simplified matching pursuit algorithm uses the linear operation of the atomic vector to gradually approach the signal vector, and the calculation of the atomic vector is a nonlinear problem. This method does not consider that the synthetic aperture radar SAR image obeys the complex distribution. , the relationship between pixels cannot be mined only through simple linear calculations, resulting in image information that cannot be fitted

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