A Sea-Land Segmentation Method for SAR Images Using Context-Aware and Edge Attention Based CNNs

  Objectives  Sea-land segmentation is of great significance for tasks such as ocean target detection and coastline extraction in synthetic aperture radar (SAR) image. To solve the problem of sea-land segmentation of multi-resolution SAR image in practical applications, this paper presents a sea-land segmentation method based on context and edge attention.

  Methods  The proposed method uses the channel attention mechanism to fuse context features of different scales and levels, and designs an edge extraction branch to provide edge information for further improving the segmentation result of the boundary area. In addition, a sea-land segmentation dataset of multi-resolution SAR image based on the Gaofen-3 satellite data is provided. The dataset covers multiple resolution images, including various sea-land boundary types such as ports, islands. Using this multi-resolution SAR image coastline segmentation dataset, we perform experimental analyses on the effectiveness of the proposed network and the contributions of each module.

  Results and Conclusions  Experimental results show that the proposed method can work well for the task of sea-land segmentation, the overall classification accuracy and mean intersection over union achieve 98.21% and 96.47%, respectively.