scholarly journals Integration of Automatic Identification System (AIS) Data and Single-Channel Synthetic Aperture Radar (SAR) Images by SAR-Based Ship Velocity Estimation for Maritime Situational Awareness

2019 ◽  
Vol 11 (19) ◽  
pp. 2196 ◽  
Author(s):  
Maria Daniela Graziano ◽  
Alfredo Renga ◽  
Antonio Moccia
Keyword(s):  
Synthetic Aperture Radar ◽  
Velocity Estimation ◽  
Synthetic Aperture ◽  
Automatic Identification ◽  
Identification System ◽  
Sar Images ◽  
Automatic Identification System ◽  
Route Information ◽  
Sar Data ◽  
Aperture Radar

The synergic utilization of data from different sources, either ground-based or spaceborne, can lead to effective monitoring of maritime activities. To this end, the integration of synthetic aperture radar (SAR) images with data reported by the automatic identification system (AIS) is of high interest. Accurate matching of ships detected in SAR images with AIS data requires compensation of the azimuth offset, which depends on the ship’s velocity. The existing procedures interpolate the route information gathered by AIS to estimate the ship’s velocity at the epoch of the SAR data, to remove the offset. Matching accuracy is limited by interpolation errors and AIS route information unavailability or uncertainties. This paper proposes the use of SAR-based ship velocity estimations to improve the integration of AIS and SAR data. A case study has been analyzed, in which the method has been tested on TerraSAR-X images collected over the Gulf of Naples, Italy. Presented results show that the matching is improved with respect to standard procedures. The proposed method limits the distance between the AIS report and the SAR-based detection to less than 150 m, which is in line with maritime surveillance needs.

scholarly journals EARTH OBSERVATION APPLICATIONS FOR GOAL 14: IMPROVING MARITIME DOMAIN AWARENESS USING SYNTHETIC APERTURE RADAR IMAGING WITH AUTOMATIC IDENTIFICATION SYSTEM IN THE PHILIPPINES

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 215-219
Author(s):  
R. Vicente ◽  
L. Tabanggay ◽  
J. Rayo ◽  
K. Mina ◽  
A. Retamar
Keyword(s):  
Synthetic Aperture Radar ◽  
The Philippines ◽  
Earth Observation ◽  
Synthetic Aperture ◽  
Automatic Identification ◽  
Identification System ◽  
Automatic Identification System ◽  
Maritime Surveillance ◽  
Maritime Domain Awareness ◽  
Aperture Radar

Abstract. The Philippines has acquired access to the NovaSAR-1 satellite developed by Surrey Satellite Technology, Ltd. (SSTL) for the implementation of its project Synthetic Aperture Radar (SAR) and Automatic Identification System (AIS) for Innovative Terrestrial Monitoring and Maritime Surveillance, which will provide simultaneous S-band SAR imaging with AIS data for applications targeted on improving maritime domain awareness. The country has inherent challenges in this field due to its archipelagic nature, with earth observation seen as a potential solution as it provides an immediate and wide coverage over designated priority areas. This contributes toward achieving Sustainable Development Goal 14: Life Below Water by providing objective information in support of data-driven decision and policymaking, closing knowledge gaps in monitoring Philippine waters.

ACFNet: A Feature Fusion Network for Glacial Lake Extraction Based on Optical and Synthetic Aperture Radar Images

2021 ◽  
Vol 13 (24) ◽  
pp. 5091
Author(s):  
Jinxiao Wang ◽  
Fang Chen ◽  
Meimei Zhang ◽  
Bo Yu
Keyword(s):  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Glacial Lake ◽  
Landsat 8 ◽  
Glacial Lakes ◽  
Sar Images ◽  
Radar Images ◽  
Optical Images ◽  
Sar Data ◽  
Aperture Radar

Glacial lake extraction is essential for studying the response of glacial lakes to climate change and assessing the risks of glacial lake outburst floods. Most methods for glacial lake extraction are based on either optical images or synthetic aperture radar (SAR) images. Although deep learning methods can extract features of optical and SAR images well, efficiently fusing two modality features for glacial lake extraction with high accuracy is challenging. In this study, to make full use of the spectral characteristics of optical images and the geometric characteristics of SAR images, we propose an atrous convolution fusion network (ACFNet) to extract glacial lakes based on Landsat 8 optical images and Sentinel-1 SAR images. ACFNet adequately fuses high-level features of optical and SAR data in different receptive fields using atrous convolution. Compared with four fusion models in which data fusion occurs at the input, encoder, decoder, and output stages, two classical semantic segmentation models (SegNet and DeepLabV3+), and a recently proposed model based on U-Net, our model achieves the best results with an intersection-over-union of 0.8278. The experiments show that fully extracting the characteristics of optical and SAR data and appropriately fusing them are vital steps in a network’s performance of glacial lake extraction.

Extreme Waves Detected by Satellite Borne Synthetic Aperture Radar

2002 ◽  
Author(s):  
Susanne Lehner ◽  
Johannes Schulz-Stellenfleth ◽  
Andreas Niedermeier ◽  
Jochen Horstmann ◽  
Wolfgang Rosenthal
Keyword(s):  
Synthetic Aperture Radar ◽  
Wave Height ◽  
Sampling Rate ◽  
Ocean Wave ◽  
Synthetic Aperture ◽  
Extreme Waves ◽  
Sar Images ◽  
Data Set ◽  
Sar Data ◽  
Aperture Radar

Within the last 20 years at least 200 supercarriers have been lost, due to severe weather conditions. In many cases the cause of accidents is believed to be ‘rouge waves’, which are individual waves of exceptional wave height or abnormal shape. I situ measurements of extreme waves are scarce and most observations are reported by ship masters after the encounter. In this paper a global set of synthetic aperture radar (SAR) images is used to detect extreme ocean wave events. The data were acquired aboard the European remote sensing satellite ERS-2 every 200 km along the track. As the data are not available as a standard product of the Europea Space Agency (ESA), the radar raw data were focused to complex SAR images using the processor BSAR developed by the German Aerospace Center. The entire SAR data set covers 27 days representing 34000 SAR imagettes with a size of 5km×10km. Complex SAR data contain information on ocean wave height, propagation direction and grouping as well as on ocean surface winds. Combining all of this information allows to extract and locate extreme waves from complex SAR images on a global basis. Special algorithms have been developed to retrieve the following parameters from the SAR data: Wind speed and direction, significant wave height, wave direction, wave groups and their individual heights. The satellite ENVISAT launched in March 2002 acquires SAR data with an even higher sampling rate (every 100 km). It is expected that a long-term analysis of ERS and ENVISAT data will give new insight into the physical processes responsible for rogue wave generation. Furthermore, the identification of hot spots will contribute to the optimization of ship routes.

scholarly journals A Novel Orthogonal Waveform Separation Scheme for Airborne MIMO-SAR Systems

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3580 ◽  
Author(s):  
Jie Wang ◽  
Ke-Hong Zhu ◽  
Li-Na Wang ◽  
Xing-Dong Liang ◽  
Long-Yong Chen
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Synthetic Aperture Radar ◽  
3D Imaging ◽  
Synthetic Aperture ◽  
Separation Scheme ◽  
Sar Images ◽  
Wide Swath ◽  
Aperture Radar

In recent years, multi-input multi-output (MIMO) synthetic aperture radar (SAR) systems, which can promote the performance of 3D imaging, high-resolution wide-swath remote sensing, and multi-baseline interferometry, have received considerable attention. Several papers on MIMO-SAR have been published, but the research of such systems is seriously limited. This is mainly because the superposed echoes of the multiple transmitted orthogonal waveforms cannot be separated perfectly. The imperfect separation will introduce ambiguous energy and degrade SAR images dramatically. In this paper, a novel orthogonal waveform separation scheme based on echo-compression is proposed for airborne MIMO-SAR systems. Specifically, apart from the simultaneous transmissions, the transmitters are required to radiate several times alone in a synthetic aperture to sense their private inner-aperture channels. Since the channel responses at the neighboring azimuth positions are relevant, the energy of the solely radiated orthogonal waveforms in the superposed echoes will be concentrated. To this end, the echoes of the multiple transmitted orthogonal waveforms can be separated by cancelling the peaks. In addition, the cleaned echoes, along with original superposed one, can be used to reconstruct the unambiguous echoes. The proposed scheme is validated by simulations.

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