Polarized SAR image classification method based on nonsubsampled contourlet convolutional neural network

Abstract

The invention discloses a polarized SAR image classification method based on a nonsubsampled contourlet convolutional neural network, and mainly at solving the problems that influence of speckle noises is hard to avoid and the classification precision is low in the prior art. The method comprises the steps that a polarized SAR image to be classified is denoised; Pauli decomposition is carried out on a polarized scattering matrix S obtained by denoising; image characteristics obtained via Pauli decomposition are combined into a characteristic matrix F, and the characteristic matrix F is normalized and recorded as F1; 22*22 blocks surrounding the F1 are taken for each pixel point to obtain a block based characteristic matrix F2; a training data set and a test data set are selected from the F2; the nonsubsampled contourlet convolutional neural network is established to train the training data set; and the trained nonsubsampled contourlet convolutional neural network is used to classify the test data set. The polarized SAR image classification method improves the expression capability and the classification precision of the features of the polarized SAR image, and can be used for target identification.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a polarimetric SAR image classification method, which can be used for target recognition. Background technique [0002] Polarization SAR is a high-resolution active microwave remote sensing imaging radar, which has the advantages of all-weather, all-time, high resolution, side-view imaging, etc., and can obtain richer information on targets. The purpose of polarimetric SAR image classification is to use the polarization measurement data obtained by airborne or spaceborne polarimetric SAR sensors to determine the category to which each pixel belongs. It has a wide range of applications in agriculture, forestry, military, geology, hydrology, and ocean research and application value. [0003] The commonly used polarimetric SAR image classification method is based on pixels, that is, only the characteristics of each pixel are used for classification. Although t...

AI Technical Summary

Problems solved by technology

One defect of H / α classification is that the division of regions is too arbitrary. When the same class of data is distributed on the boundary of two or more classes, the performance of the classifier will deteriorate. Another shortcoming is that when the data in the same area When several different features coexist, they cannot be effectively distinguished;

[0006] In 2004, Lee et al. proposed a feature extraction method based on Freeman decomposition. This method can maintain various polarization scattering characteristics, but the classification results are easily affected by the performance of Freeman decomposition. For polarization data of different bands, the algorithm poor universality

[0007] These feature extraction methods do not take into account the multi-scale and multi-resolution characteristics of polarimetric SAR images, and it is difficult to obtain high classification accuracy for polarimetric SAR images with complex backgrounds.

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