Multi-Scale Graph Convolutional Network and Dynamic Iterative Class Loss for Ship Segmentation in Remote Sensing Images

Abstract

The accuracy of the semantic segmentation results of ships is of great significance to coastline navigation, resource management, and territorial protection. Although the ship semantic segmentation method based on deep learning has made great progress, there is still the problem of not exploring the correlation between the targets. In order to avoid the above problems, this paper designed a multi-scale graph convolutional network and dynamic iterative class loss for ship segmentation in remote sensing images to generate more accurate segmentation results. Based on DeepLabv3+, our network uses deep convolutional networks and atrous convolutions for multi-scale feature extraction. In particular, for multi-scale semantic features, we propose to construct a Multi-Scale Graph Convolution Network (MSGCN) to introduce semantic correlation information for pixel feature learning by GCN, which enhances the segmentation result of ship objects. In addition, we propose a Dynamic Iterative Class Loss (DICL) based on iterative batch-wise class rectification instead of pre-computing the fixed weights over the whole dataset, which solves the problem of imbalance between positive and negative samples. We compared the proposed algorithm with the most advanced deep learning target detection methods and ship detection methods and proved the superiority of our method. On a High-Resolution SAR Images Dataset [1], ship detection and instance segmentation can be implemented well.