Polarization SAR image classification based on RBM and SVM

Abstract

The invention discloses a polarization SAR image classification method based on RBM and SVM, mainly solving the problem of the existing polarization SAR image classification method that the classification precision is low. The method comprises the steps as follows: (1) inputting the polarization SAR image to be classified, having delicate polarization Lee filtering operation; (2) resolving and extracting original feature of each pixel point and integrating based on the polarization coherence matrix, polarization covariance matrix and Cloude; (3) initializing and training RBM to obtain the related parameter; (4) classifying by using SVM according to the feature learned by the RBM to obtain the classification result. Compared with the existing method, the space dependency of the image is totally considered and the feature in favour of classifying polarization SAR image can be extracted, the polarization SAR image classification precision is obviously raised, the method can be used for terrain classification and object identification for polarization SAR image.

Description

technical field [0001] The invention belongs to the technical field of image processing and relates to applications in the field of image classification, in particular to a polarimetric SAR image classification method based on RBM and SVM, which can be used for object classification and target recognition of polarimetric SAR images. Background technique [0002] Polarization SAR is a high-resolution active coherent multi-channel microwave remote sensing imaging radar. It is an important branch of SAR. It has the advantages of all-weather, all-time, high resolution and side-view imaging. Agriculture, navigation, geographic surveillance and many other fields. Polarimetric SAR can obtain richer target information, and is highly valued in the field of international remote sensing. Therefore, polarimetric SAR image classification has become an important research direction of polarimetric SAR information processing. [0003] The existing polarization SAR image classification meth...

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Problems solved by technology

[0005] Unsupervised classification methods include: H / α unsupervised classification proposed by Cloude et al., which obtains scattering entropy H and average scattering angle α characteristic parameters through Cloude target decomposition, and performs eight classifications on targets according to the range of these two parameters. In this method, the classification boundary is fixed and the division of regions is too arbitrary, and only the two parameters H and α are used, and the polarization information is not fully utilized, resulting in low classification accuracy; Lee et al. proposed the H method based on Cloude target decomposition and Wishart classifier. / α-Wishart unsupervised classification method, which adds Wishart iteration on the basis of the original H / α classification, which makes up for the defects of the fixed boundary of H / α classification, but this method cannot well maintain all kinds of polarization scattering Characteristics; Lee et al. proposed a polarimetric SAR image classification method based on Freeman decomposition, which mainly divides the polarization data according to the size of plane scattering power, dihedral scattering power and volume scattering power obtained by Freeman decomposition, and the initial Classification is performed to merge categories, and finally the Wishart classifier is used to reclassify. This method maintains the scattering characteristics of various types, but there are multi-category divisions and mergers, and the computational complexity is high.

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