A polarimetric SAR image ground recognition method based on U-Net network combined with multiple attention mechanisms
Aiming at the problem that polarization information is difficult to use,convolutional neural network(CNN)only focuses on the information in the local receptive field,and cannot accurately extract the key features,which leads to the degradation of the performance of recognition tasks,a method based on U-Net network combined with multiple attention modules(MA U-Net)was proposed in this paper.The time series of polarization state was converted into frequency representation through joint time-frequency analysis(JTFA),the frequency components of the signal was revealed,helps to extract useful information,and reduces the difficulty of using polarization information.In addition,the U-Net network with multiple attention modules was used for feature extraction,and the ResNet network was used for feature recognition.Compared with the traditional CNN and U-Net networks,the proposed method had higher recognition accuracy in the same data set,and the average recognition accuracy is improved by 6.1％and 4.5％respectively,which showed obvious advantages in polarimetric synthetic aperture radar(SAR)image target recognition.
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