Data Association Techniques for Near-Contemporaneous SAR and AIS Datasets from NovaSAR-1

Rodger, Maximilian; Guida, Raffaella

doi:10.1109/igarss.2019.8898207

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…The rank-ordered assignment algorithm demonstrated its robustness by automatically correcting itself after determining the top-best assignments, corroborating previous simulation results [43]. Figure 20 shows a scenario similar to the conceptual example shown in Figure 6.…”

Section: Effectiveness Of Rank-ordered Assignmentsupporting

confidence: 85%

“…Moreover, the value selected for m is three meanings for which, for a given SAR ship detection, the top 3-best assignments in AIS D are returned. The performance of this particular technique has been quantitatively assessed in a previous publication [43].…”

Section: Data Associationmentioning

confidence: 99%

“…The basic principle involves recovering the spatial transformation that aligns the positions of the two sets of observations (or point sets). In a previous publication, it is demonstrated that the performance of all of these techniques depends primarily on the temporal difference (or 'gap') between the sensor acquisitions as well as the ship density [43]. In congested areas, ships detected in SAR imagery can be wrongly associated with AIS observations resulting in mis-associations.…”

Section: Introductionmentioning

confidence: 99%

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Classification-Aided SAR and AIS Data Fusion for Space-Based Maritime Surveillance

Rodger

Guida

2020

Remote Sensing

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A wide range of research activities exploit spaceborne Synthetic Aperture Radar (SAR) and Automatic Identification System (AIS) for applications that contribute to maritime safety and security. An important requirement of SAR and AIS data fusion is accurate data association (or correlation), which is the process of linking SAR ship detections and AIS observations considered to be of a common origin. The data association is particularly difficult in dense shipping environments, where ships detected in SAR imagery can be wrongly associated with AIS observations. This often results in an erroneous and/or inaccurate maritime picture. Therefore, a classification-aided data association technique is proposed which uses a transfer learning method to classify ship types in SAR imagery. Specifically, a ship classification model is first trained on AIS data and then transferred to make predictions on SAR ship detections. These predictions are subsequently used in the data association which uses a rank-ordered assignment technique to provide a robust match between the data. Two case studies in the UK are used to evaluate the performance of the classification-aided data association technique based on the types of SAR product used for maritime surveillance: wide-area and large-scale data association in the English Channel and focused data association in the Solent. Results show a high level of correspondence between the data that is robust to dense shipping or high traffic, and the confidence in the data association is improved when using class (i.e., ship type) information.

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Section: Effectiveness Of Rank-ordered Assignmentsupporting

confidence: 85%

Section: Data Associationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Classification-Aided SAR and AIS Data Fusion for Space-Based Maritime Surveillance

Rodger

Guida

2020

Remote Sensing

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“…S-band SAR may be expected to serve as a trade-off between high and low-frequency SAR systems to overcome the limitations of both high and low-frequency SAR systems up to some extent [5]. For example it is found successful in detection of below-canopy inundation in oodplain forests due to the specular doublebounce scattering in the tropical forest and wetlands regions of the Amazon Basin [6], maritime Surveillance [7], [8], terrestrial and marine environments [9] and data association or fusion techniques for object identi cations [10].…”

Section: Introductionmentioning

confidence: 99%

Separability Analysis of Back-Scattering Coefficient of NovaSAR-1 S-band SAR datasets for different Land Use Land Cover (LULC) classes

Bhardwaj

Saini

Chatterjee

2021

Preprint

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NovaSAR-1 is a joint technology initiative of SSTL (Surrey Satellite Technology Ltd.), UK, and Airbus DS (former EADS Astrium Ltd, Stevenage, UK). The NovaSAR-1 mini-satellite was launched on 16 September 2018 and it is operating on the S-band frequency range, which is less common in Spaceborne Synthetic Aperture Radar (SAR) systems. Both higher and lower SAR frequency bands (L-band & X-band SAR) have their advantages as well as limitations in different kinds of applications. High frequency (X-band) SAR systems are useful for top surface information extraction such as the DSM generation. However, at the same time, more noise and lesser coherence issues are associated with high-frequency SAR systems. Low-frequency SAR (L-band) systems exhibit better ground penetration, high coherence, and low noise, but less precise scatterer level information. The S-band comes approximately in the middle of the X and L-band SAR frequency range and may be used as a trade-off between high and low-frequency SAR systems to have some advantages. In the presented study, the separability analysis of the radar backscattering coefficient of HH polarization (Stripmap and ScanSAR) of NovaSAR-1 S-band datasets corresponding to different land use and land covers (LULCs) has been done to analyze the potential of NovaSAR-1 S-band SAR data. The analysis was carried out for datasets acquired between 9th July 2019 to 15th July 2019 at 5 experimental sites in parts of six different Indian states (West Bengal, Maharashtra, Jharkhand, Odisha, Chhattisgarh, and Uttar Pradesh). The statistical analysis of σ० for five different sites of India for different LULCs, such as bare soil, forest, water, urban, cropland, and road features has been carried out. The range for minimum and maximum mean σ० values for urban, sub-urban, cropland, bare soil, barren land, forest, turbid water, road features, water with a smooth surface (calm water), and road features (airplane runway) were found to be -5.45 to 4.76, -11.14 to -5.21, -17.25 to -5.99, -18.15 to -13.44, -17.64 to -9.34, -17.17 to -14.34, -18.2 to -14.05, -27.29 to -23.76 and − 26.64 to -14.98 respectively. The range of σ० pixel values of calibrated datasets corresponding to different LULCs depicted that the data quality is good for the identification of various land covers. The separability analysis of the different land cover classes depicted that classes have good separability except for a few pairs of LULC. With the availability of fully polarimetric and InSAR data in the planned NISAR mission, the polarimetric scattering behaviour with phase information for InSAR will further be utilized.

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SAR and AIS Data Fusion for Dense Shipping Environments

Rodger

Guida

2020

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

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Data Association Techniques for Near-Contemporaneous SAR and AIS Datasets from NovaSAR-1

Cited by 6 publications

References 10 publications

Classification-Aided SAR and AIS Data Fusion for Space-Based Maritime Surveillance

Classification-Aided SAR and AIS Data Fusion for Space-Based Maritime Surveillance

Separability Analysis of Back-Scattering Coefficient of NovaSAR-1 S-band SAR datasets for different Land Use Land Cover (LULC) classes

SAR and AIS Data Fusion for Dense Shipping Environments

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