Method for finely classifying polarized SAR images based on Freeman entropy and self-learning

Abstract

The invention discloses a method for finely classifying polarized SAR images based on Freeman entropy and self-learning. The problems that in existing supervised classification, surface feature labels are difficult to obtain, and shadow regions and mixing scattering regions are difficult to distinguish are mainly solved. The implementation process of the method comprises the steps that (1) eigenvalue decomposition is carried out on a polarization coherence matrix to obtain three characteristic values; (2) decomposition is carried out on a polarization covariance matrix to obtain three kinds of scattered power; (3) characteristic vectors are input according to the three characteristic values and a volume scattered power structure; (4) spectral clustering is carried out on the input characteristic vectors of random sampling points; (5) SVM classification is carried out according to the sampling points and the clustering marks of the sampling points; (6) MRF iteration is carried out on a classification result; (7) spectral clustering is carried out on wrongly-classified pixel points, and the fine classification surface feature categories of the polarized SAR images is obtained. Compared with an existing SAR image classification method, the method does not need manual label defining, the classification result is more precise, and the method can be used for target detection and classification recognition of the polarized SAR images.

Description

Technical field [0001] The invention belongs to the technical field of image processing, relates to polarization synthetic aperture radar image classification, and can be used for image target detection and image target classification and recognition. Background technique [0002] With the increasing development of radar technology, polarized SAR has become the development trend of SAR, and polarized SAR can obtain richer target information. The understanding and interpretation of polarized SAR images involves many subjects such as signal processing and pattern recognition. Polarized SAR image classification is one of the basic problems of polarized SAR image processing, laying the foundation for the later recognition of polarized SAR image. [0003] The existing polarization SAR image classification can be divided into two categories: unsupervised clustering and supervised classification. [0004] Unsupervised clustering methods include the use of scattering entropy, scattering an...

AI Technical Summary

Problems solved by technology

[0005] Supervised classification methods include: Kong et al. proposed to classify polarimetric SAR images using statistical information of data, which has strict requirements on data distribution; Hellmann et al. proposed to use neural network classifier to classify. The convergence speed of the method is slow, and it is easy to fall into local optimum

At the same time, since it is difficult to obtain real ground object labels in polarimetric SAR images, manual definition of labels is costly and inaccurate

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