IceRegionShip: Optical Remote Sensing Dataset for Ship Detection in Ice-Infested Waters

• Published in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Main Authors: Peilin Wang, Bingxin Liu, Ying Li, Peng Chen, Peng Liu
• Format: Article in Journal/Newspaper
• Language: English
• Published: IEEE 2024
• Subjects: Deep learning; ice-infested waters (IIW); remote sensing dataset; ship detection (SD); Ocean engineering; TC1501-1800; Geophysics. Cosmic physics; QC801-809; Sea ice
• Online Access: https://doi.org/10.1109/JSTARS.2023.3335294; https://doaj.org/article/0089538828e64459a8531d3d16c993db

As shipping routes and resource exploration move toward high-latitude oceans, sea ice becomes a major threat to the safety of ship navigation, posing significant challenges to the shipping industry and offshore resource development. Continuous development of satellite remote sensing and deep learning has made large-scale and wide-ranging ship detection (SD) possible, which is of great significance for ship safety. However, existing ship datasets used for deep learning only include ship images in open waters (OW), such as ports and inland rivers. Currently, remote sensing datasets suitable for SD in ice-infested waters (IIW) are lacking. SD in IIW is more difficult than SD in OW because of complex background interference from sea ice. Thus, it is infeasible to directly use the features of ships in OW for SD in IIW. Herein, we propose a remote sensing SD dataset called IceRegionShip, which includes subdatasets IceRegionShip–red, green and blue (RGB) and IceRegionShip–ice region ship index (IRSI). IceRegionShip–IRSI consists of low-resolution images processed with IRSI. IceRegionShip–RGB and IceRegionShip–IRSI contain 11 436 and 9073 ship instances, respectively. IRSI was proposed to address false alarms caused by ice interference. To the best of our knowledge, this is the first dataset designed specifically for SD in IIW. In addition, the dataset was evaluated using several advanced detection algorithms, providing a benchmark for SD in IIW and demonstrating the effectiveness of IRSI for SD in low-resolution optical remote sensing images.