Ship Segmentation on High-Resolution Sar Image by a 3D Dilated Multiscale U-Net
Jichao Li, Chubing Guo, Shuiping Gou, Yuanbo Chen, Miao Wang, Jiawei Chen · 2020
Targets detection and segmentation in a synthetic aperture radar (SAR) image is a vital step for its interpretation. It is quite challenging for most conventional methods due to complex background and the speckle. Furthermore, the sizes of targets in a scene are variable. Inspired by the success of neural networks in computer vision, In this paper, we propose a 3D dilated multi-scale U-shape convolutional neural network (3DDM-UNet). In the proposed method, we first build a 3D image block via a multiscale stationary wavelet transform to exploit the structural information of targets with various sizes. Then, the built 3D image block is fed into a 3D dilated multiscale U-Net. To train the proposed network, we build a dataset from a scene of SAR image with various sizes and shapes of ship targets. Finally, the trained network is employed to the testing set to obtain the segmentation results. Experimental results on test images show that the proposed method achieved better performance than conventional methods.