Multi-scale Convolutional Neural Network for SAR Image Semantic Segmentation

Although the recent success of convolutional neural networks (CNNs) greatly advance the semantic segmentation of the natural images, few work has focused on the remote sensing images, especially the synthetic aperture radar (SAR) images. Specifically, the existing methods do not consider the speckle noise of the SAR images and the multi-scale characteristics contained in the SAR images. In this paper, we propose a multiscale convolutional neural network (CNN) model for SAR image semantic segmentation. The multi-scale CNN model includes noise removal stage, convolutional stage, feature concatenation stage and classification stage. In particular, we construct a sparse representation loss function to obtain a clear SAR image in noise removal stage. Then, the multi-scale convolutional stage is employed to learn the multi-scale deep features. The concatenation stage is used to connect the features with different scales and depths. Finally, softmax classifier is developed to obtain the labels of the SAR images with the multi-scale CNN model being trained in an end-to-end way. The experimental results on synthetic and real SAR images demonstrate the effectiveness of the proposed method.