Classification of Multi-Channel SAR Data Based on MB-U 2 -ACNet Model for Shanghai Nanhui Dongtan Intertidal Zone Environment Monitoring

Recently, deep learning has already shown its availability in synthetic aperture radar (SAR) image classification. To improve the deep learning model's performance on multisource remote sensing information fusion, we propose a multi-branch deep convolutional neural network model specially tailored from the U2-Net framework and with asymmetric convolutions. We name it the MB-U2-ACNet model. Based on experiments on a constructed dataset dedicated for Shanghai Nanhui Dongtan intertidal zone environment monitoring, it is verified that the proposed MB-U2-ACNet model has better performance than the existing representative deep and traditional methods.