Polarization SAR ground object classification method based on self-step learning convolutional neural network

Abstract

The present invention discloses a polarization SAR ground object classification method based on a self-step learning convolutional neural network, in order to mainly solve the problems that the priorart has low accuracy in classifying complex ground object scenes and is heavily affected by noise. The implementation scheme comprises: 1, obtaining a polarization scattering matrix S and a pseudo color RGB image under the Pauli basis from original complete polarization SAR data; 2, constructing a three-dimensional matrix to form a sample set for each pixel, and constructing a training sample setand a test sample set; 3, constructing a convolutional neural network and training the convolutional neural network based on self-step learning to accelerate network convergence and improve the generalization ability of the network; and 4, classifying the test samples by using the trained convolutional neural network to obtain a final complete polarization SAR ground object classification result.According to the method disclosed by the present invention, accuracy for classifying the target ground objects of complex ground object scenes in the polarization SAR image is improved, and the methodcan be used for feature classification and target recognition.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a polarimetric SAR object classification method, which is applicable to object classification and target recognition. Background technique [0002] With the development of microwave remote sensing technology, high-resolution polarization synthetic aperture radar has become an inevitable trend in the development of SAR field, and polarization SAR image classification, as one of the important methods of polarization SAR image interpretation, has been widely used in national defense and civilian applications. and many other fields. Although high-resolution polarization SAR contains rich backscatter information, the current classification algorithms only use shallow polarization features, which cannot fully represent the complex scene information contained in the image. [0003] The classification of polarimetric SAR images involves many disciplines such as statistica...

AI Technical Summary

Problems solved by technology

However, the disadvantage of this method is that there is still a lack of better selection criteria for the polarization characteristic parameters, and only using the polarization characteristic parameters of the SAR image cannot fully express the image, which will directly affect the final result of the classification

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