Shipnet for Semantic Segmentation on VHR Maritime Imagery
Shihao Sun, Zexin Lu, Wenjie Liu, Wei Hu, Ruirui Li · 2018
For VHR maritime images, sematic segmentation is a new research hotspot and plays an important role in coastline navigation, resource management and territory protection. Without enough labeled training data, it is a challenge to separate small objects on a large scale while segment the big area clearly. To deal with it, we propose a novel ShipNet and design a weighted loss function for simultaneous sea-land segmentation and ship detection. To prove the proposed method, we also built and opened a new dataset to the community which contains VHR multiscale maritime images. Compared with the FCN and ResNet, the proposed method got much better F1 scores 85.90% for ship class and 97.54% overall accuracy. Compared with multiscale FCN, the ShipNet could obtain details results like sharp edges. Even for images with bad quality, the ShipNet could also keep robust and get good results.