OpenSARShip: A Dataset Dedicated to Sentinel-1 Ship Interpretation
By Lanqing Huang, Bin Liu, Boying Li, Weiwei Guo, Wenhao Yu, Zenghui Zhang, Wenxian Yu. 2018
379 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Media Technology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
In Computer Vision and Pattern Recognition
- Lichao Mou
- Andreas R. Dengel
- Xiaoxiang Zhu
- Benjamin Bischke
- Patrick Helber
- Gang Zheng
- Richard Bamler
- Yuhai Liu
In Media Technology
- Xiaoxiang Zhu
- Lichao Mou
- Andreas R. Dengel
- Richard Bamler
- Damian Borth
- Yuanyuan Wang
- Feng Xu
- Patrick Helber
Citations in the corpus
- EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification (2017, background): “However, to fully utilize the data for such applications, satellite images must first be processed and transformed into structured semantics [35].”
- Fine-Grained Continual Learning for SAR Target Recognition (2022, methodology): “To evaluate the performance of the proposed method, the OpenSARShip dataset [7] is used.”
- SkySense: A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagery (2023, background): “In contrast, SAR sensors cap-1 ture clear imagery in all weather conditions [34, 42].”
- HRSID: A High-Resolution SAR Images Dataset for Ship Detection and Instance Segmentation (2020, neutral): “OpenSAR-ship [41] has 10 categories.”
- Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives (2020, methodology): “nals. However, considering the scarcity of such datasets, MSTAR is a valuable source for target recognition. OpenSARShip 2.0 [194]: This dataset was built based on its previous version, OpenSARShip [195]. It contains 34,528 Sentinel-1 SAR image patches of different ships with automatic identificatio”
- Deep-learning-based information mining from ocean remote-sensing imagery (2020, methodology): “TheAVISO SSH data are produced and distributed by Aviso+ (https://www.aviso. altimetry.fr/), as part of the Ssalto ground-processing segment; the satellite-tracked drifter data were provided by the GDP Drifter Data Assembly Center (DAC) at NOAA’s Atlantic Oceanographic and Meteorological Laboratory ”