Residual Convolutional Neural Network With Autoencoder Based Attention For PolSAR Image Classification

The polarimetric synthetic aperture radar (PolSAR) images contain fine characteristics and abstract spatial features, which attention to them can improve the classification accuracy. In this work, the residual convolutional neural network with autoencoder based attention (RCNN-AA) is proposed for PolSAR image classification. The scaled difference of the convolutional autoencoder with the original input patch is used as the weight, which contains information about the fine spatial features. Multiplication of this normalized difference in the input patch provides the attention feature maps that can be concatenated with the original input and used as input of the RCNN. An ablation study is done, and also, the proposed RCNN-AA model is compared to some deep learning based models. The results show preference of the RCNN-AA with respect to the competitors.