TY - JOUR
T1 - OpenSARShip
T2 - A dataset dedicated to Sentinel-1 ship interpretation
AU - Huang, Lanqing
AU - Liu, Bin
AU - Li, Boying
AU - Guo, Weiwei
AU - Yu, Wenhao
AU - Zhang, Zenghui
AU - Yu, Wenxian
N1 - Funding Information:
Manuscript received June 22, 2017; revised August 25, 2017; accepted September 17, 2017. Date of publication October 12, 2017; date of current version January 12, 2018. This work was supported by the State Key Program of the National Natural Science Foundation of China under Grant 61331015. (Corresponding author: Bin Liu.) The authors are with the Shanghai Key Laboratory of Intelligent Sensing and Recognition, Shanghai Jiao Tong University, Shanghai 200240, China (e-mail: [email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected]).
Publisher Copyright:
© 2017 IEEE.
PY - 2018/1
Y1 - 2018/1
N2 - With the rapid growth of Sentinel-1 synthetic aperture radar (SAR) data, how to exploit Sentinel-1 imagery and achieve effective and robust marine surveillance are crucial problems. In this paper, we present the OpenSARShip, a dataset dedicated to Sentinel-1 ship interpretation. The OpenSARShip, providing 11 346 SAR ship chips integrated with automatic identification system messages, owes five essential properties: specificality, large scale, diversity, reliability, and public availability. These properties make sure that the OpenSARShip achieves its objectives. The first is to provide researchers a benchmark dataset to develop applicable and adaptive ship interpretation algorithms and push the performance ceilings of data analysis. The other is to provide a dataset for performing application-oriented quality assessment for Sentinel-1 imagery, which can boost their applications in a targeting way. The construction and the organization of the OpenSARShip are discussed, which show the inside of the dataset and ensure the essential properties. The elaborate geometric and scattering analyses, the benchmark for classification, and the imagery applicability assessment by using the OpenSARShip all demonstrate the applicability and potential of the dataset.
AB - With the rapid growth of Sentinel-1 synthetic aperture radar (SAR) data, how to exploit Sentinel-1 imagery and achieve effective and robust marine surveillance are crucial problems. In this paper, we present the OpenSARShip, a dataset dedicated to Sentinel-1 ship interpretation. The OpenSARShip, providing 11 346 SAR ship chips integrated with automatic identification system messages, owes five essential properties: specificality, large scale, diversity, reliability, and public availability. These properties make sure that the OpenSARShip achieves its objectives. The first is to provide researchers a benchmark dataset to develop applicable and adaptive ship interpretation algorithms and push the performance ceilings of data analysis. The other is to provide a dataset for performing application-oriented quality assessment for Sentinel-1 imagery, which can boost their applications in a targeting way. The construction and the organization of the OpenSARShip are discussed, which show the inside of the dataset and ensure the essential properties. The elaborate geometric and scattering analyses, the benchmark for classification, and the imagery applicability assessment by using the OpenSARShip all demonstrate the applicability and potential of the dataset.
KW - OpenSARShip dataset
KW - Sentinel-1
KW - Ship interpretation
KW - Synthetic aperture radar (SAR)
UR - https://www.scopus.com/pages/publications/85040909329
U2 - 10.1109/JSTARS.2017.2755672
DO - 10.1109/JSTARS.2017.2755672
M3 - Article
AN - SCOPUS:85040909329
SN - 1939-1404
VL - 11
SP - 195
EP - 208
JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
IS - 1
M1 - 8067489
ER -
