Sentinel-1 near real-time application for maritime situational awareness

被引:0
|
作者
Detmar Krause
Egbert Schwarz
Sergey Voinov
Heiko Damerow
Daniel Tomecki
机构
[1] National Ground Segment (NBS),German Remote Sensing Data Center (DFD)
来源
CEAS Space Journal | 2019年 / 11卷
关键词
Near Real time; Maritime; Ship; Wind; Wave; SAR;
D O I
暂无
中图分类号
学科分类号
摘要
In the context of the project real-time services for maritime security (Echtzeitdienste für die Maritime Sicherheit—security), an experimental research platform for validation of maritime products derived from remote sensing data, was developed. This article describes the work carried out to derive ship-, wind-, and wave detection products out of Sentinel-1 remote-sensing data by DLR’s Maritime Safety and Security Lab in Neustrelitz, part of the German Remote Data Center DFD. The activity aims to the fulfilment of project requirements, primarily to support the need for near real-time performance up to 15 min, as those in maritime situational awareness. The development and implementation cover the task of level 0 processing, based on DLR’s front end processor, the implementation of the framework for real-time processing up to level 2 (value adding), as well as the development of a hardware-independent virtual-processing platform.
引用
收藏
页码:45 / 53
页数:8
相关论文
共 50 条
  • [1] Sentinel-1 near real-time application for maritime situational awareness
    Krause, Detmar
    Schwarz, Egbert
    Voinov, Sergey
    Damerow, Heiko
    Tomecki, Daniel
    [J]. CEAS SPACE JOURNAL, 2019, 11 (01) : 45 - 53
  • [2] Near Real Time Applications for Maritime Situational Awareness
    Schwarz, E.
    Krause, D.
    Berg, M.
    Daedelow, H.
    Maass, H.
    [J]. 36TH INTERNATIONAL SYMPOSIUM ON REMOTE SENSING OF ENVIRONMENT, 2015, 47 (W3): : 999 - 1003
  • [3] Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data
    Bazzi, Hassan
    Baghdadi, Nicolas
    Fayad, Ibrahim
    Zribi, Mehrez
    Belhouchette, Hatem
    Demarez, Valerie
    [J]. REMOTE SENSING, 2020, 12 (09)
  • [4] Near Real-Time Wildfire Progression Monitoring with Sentinel-1 SAR Time Series and Deep Learning
    Yifang Ban
    Puzhao Zhang
    Andrea Nascetti
    Alexandre R. Bevington
    Michael A. Wulder
    [J]. Scientific Reports, 10
  • [5] Near Real-Time Wildfire Progression Monitoring with Sentinel-1 SAR Time Series and Deep Learning
    Ban, Yifang
    Zhang, Puzhao
    Nascetti, Andrea
    Bevington, Alexandre R.
    Wulder, Michael A.
    [J]. SCIENTIFIC REPORTS, 2020, 10 (01)
  • [6] Optimizing Near Real-Time Detection of Deforestation on Tropical Rainforests Using Sentinel-1 Data
    Doblas, Juan
    Shimabukuro, Yosio
    Sant'Anna, Sidnei
    Carneiro, Arian
    Aragao, Luiz
    Almeida, Claudio
    [J]. REMOTE SENSING, 2020, 12 (23) : 1 - 31
  • [7] Near Real-Time Freeze Detection over Agricultural Plots Using Sentinel-1 Data
    Fayad, Ibrahim
    Baghdadi, Nicolas
    Bazzi, Hassan
    Zribi, Mehrez
    [J]. REMOTE SENSING, 2020, 12 (12)
  • [8] Near Real-Time Flood Inundation Prediction Using Sentinel-1 Imagery and Deep Learning
    Mohamadiazar, Nasim
    Ebrahimian, Ali
    Hosseiny, Hossein
    [J]. WORLD ENVIRONMENTAL AND WATER RESOURCES CONGRESS 2024: CLIMATE CHANGE IMPACTS ON THE WORLD WE LIVE IN, 2024, : 824 - 834
  • [9] Near real-time monitoring of tropical forest disturbance by fusion of Landsat, Sentinel-2, and Sentinel-1 data
    Tang, Xiaojing
    Bratley, Kelsee H.
    Cho, Kangjoon
    Bullock, Eric L.
    Olofsson, Pontus
    Woodcock, Curtis E.
    [J]. REMOTE SENSING OF ENVIRONMENT, 2023, 294
  • [10] A Maritime Situational Awareness Framework Using Dynamic 3D Reconstruction in Real-Time
    Sattler, Felix
    Barnes, Sarah
    Stephan, Maurice
    [J]. 2023 27TH INTERNATIONAL CONFERENCE INFORMATION VISUALISATION, IV, 2023, : 334 - 339