FusionNet: Edge Aware Deep Convolutional Networks for Semantic Segmentation of Remote Sensing Harbor Images

Abstract

Sea–land segmentation and ship detection are two prevalent research domains for optical remote sensing harbor images and can find many applications in harbor supervision and management. As the spatial resolution of imaging technology improves, traditional methods struggle to perform well due to the complicated appearance and background distributions. In this paper, we unify the above two tasks into a single framework and apply the deep convolutional neural networks to predict pixelwise label for an input. Specifically, an edge aware convolutional network is proposed to parse a remote sensing harbor image into three typical objects, e.g., sea, land, and ship. Two innovations are made on top of the deep structure. First, we design a multitask model by simultaneously training the segmentation and edge detection networks. Hierarchical semantic features from the segmentation network are extracted to learn the edge network. Second, the outputs of edge pipeline are further employed to refine entire model by adding an edge aware regularization, which helps our method to yield very desirable results that are spatially consistent and well boundary located. It also benefits the segmentation of docked ships that are quite challenging for many previous methods. Experimental results on two datasets collected from Google Earth have demonstrated the effectiveness of our approach both in quantitative and qualitative performance compared with state-of-the-art methods.