PolSAR Terrain Classification Based on Transformer-Unet Hybrid Model in the Complex Domain
The traditional deep learning-based Polarimetric Synthetic Aperture Radar(PolSAR)feature classification method extracts image local features by stacking convolutional layers,which makes it difficult to establish long-range dependencies.It is not-ed that Transformer,a deep learning model based on a self-attention mechanism that captures global pixel-to-pixel correlations,has achieved success in image classification tasks.Meanwhile,the PolSAR feature classification task has demonstrated better classifica-tion results in the complex domain compared to the real domain.Therefore,Transformer is introduced into the complex domain,and a hybrid model of Transformer and Unet based on the complex domain(CT-Unet)is proposed for PolSAR feature classification.This model combines Transformer with CNN for feature extraction on PolSAR data of complex type.The experimental results of PolSAR feature classification using the Xi'an dataset and German dataset show that the proposed model can effectively improve the accuracy of PolSAR feature classification.Transformer is expected to make up for the shortcomings of convolutional neural net-works in the PolSAR feature classification task.
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