A novel model for edge aware sea-land segmentation

摘要

Sea-land segmentation is one of important research domains in the remote sensing image processing. Edge aware of sealandsegmentation is one of hot-points. Edge information is used as an auxiliary learning to provide more information forthe segmentation. In this paper, we propose a novel model for the sea-land segmentation with an edge detection in thelower layers and segmentation in higher layers, which is proved as an effective way to fuse the different tasks. We exploitpre-trained VGG16 model to initial the backbone. We use F-score to assess the segment output. Land accuracy is 0.9929of F-score and sea accuracy score is 0.9937 of F-score in our own test dataset in the sea-land segmentation, which is thehighest score among the five methods we take in the comparisons.