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Analyzing the dependence between RADARSAT-1 vessel detection and vessel heading using CFAR algorithm for use on fishery management
Abstract
The National Oceanic and Atmospheric Administration (NOAA) National Environment Satellite, Data, and Information Service (NESDIS) provides synthetic aperture radar (SAR) derived products under a demonstration project named the Alaska SAR Demonstration (AKDEMO) to the US government community. The AKDEMO near real-time data and products include SAR wind images and vectors, hard target locations, and ancillary data. The hard target locations are available for use in fishery management by agencies such as the Alaska Department of Fish and Games (ADF&G), the National Marine Fisheries Service ( NMFS) and the United States Coast Guard (USCG). Vessel positions are obtained form hard target signatures through the use of a constant false alarm rate (CFAR) vessel detection algorithm developed by Veridian Systems Division. This algorithm has gone through testing and validation, using fleet information and vessel observer reports, during the Red King Crab fisheries in Alaska in 1999 and 2000. The goal was to maximize the number of ships found while minimizing the number or false alarms. Using general fleet location information, it was found that the minimum vessel size detected by the CFAR algorithm was 36 m using RADARSAT-1 ScanSAR wide mode data with a nominal spatial resolution of 100 m. Still, when comparing the CFAR results with the actual positions reported by the ship observers, vessels over 36 m were not always detected. This led to the hypothesis that the heading and perhaps wind conditions may have affected the ability of the SAR to detect the vessels. In 2001, vessel observers again reported their positions during SAR overpasses, this time also reporting heading and wind conditions. Unfortunately, due to high winds and waves, SAR was not able to detect the fishing fleet. In 2002, this was repeated, resulting in 3 days during the fishery opening when RADARSAT-1 was able to image the fishing fleet in the ScanSAR wide B mode. Approximately twenty ships each day in the a rea covered by the RADARSAT-1 data reported their position and heading. Results showing the dependence between RADARSAT-1 vessel detection and vessel heading are presented using the GIS platform.
... This aim is approached by the present work through proposing a custom algorithm for ship detection adapted to three different SAR missions: Sentinel-1, SAOCOM, and COSMO-SkyMed. The algorithm uses the fast and efficient constant false alarm rate (CFAR) [19][20][21][22] together with the sub-look analysis (SLA) [23][24][25][26] discrimination technique. There is a wide corpus of research dealing with ship detection in SAR images, and the detection techniques in SAR imagery are influenced by several different key parameters, but the research on SAR ship detection can be divided into categories based on the physical property exploited. ...
In the framework of maritime surveillance, vessel detection techniques based on spaceborne synthetic aperture radar (SAR) images have promoted extensive applications for the effective understanding of unlawful activities at sea. This paper deals with this topic, presenting a novel approach that exploits a cascade application of a pre-screening algorithm and a discrimination phase. Pre-screening is based on a constant false alarm rate (CFAR) detector, whereas discrimination exploits sub-look analysis (SLA). For the first time, the method has been validated with experiments on multi-frequency (C-, X-, and L-band) SAR images, demonstrating a significant reduction of up to 40% in false alarms within highly congested scenarios, along with a notable enhancement of the receiving operating characteristic (ROC) curves. For future synergic exploitation of multiple SAR missions, the developed dataset, composed of Sentinel-1, SAOCOM, and COSMO-SkyMed images, is comprehensive, having images gathered over the same area with a short time lag (below 15 min). Finally, the diversified processing chains and the results for each mission product and scenario are discussed. Being the first dataset of single-look complex (SLC) SAR multi-frequency data, the present work intends to encourage additional investigation in this promising field of research.
In the framework of maritime surveillance, vessel detection techniques based on spaceborne Synthetic Aperture Radar (SAR) images have promoted extensive applications for the effective understanding of unlawful activities at sea. The paper deal with this topic presenting a novel approach exploiting a cascade application of a pre-screening, and discrimination phase. The latter leverage the SAR spectrum by means of sub-looks analysis. For the first time, the method has been validated with experiments on multi-frequency (C-, X-, and L-band) SAR images. For a future syngergic exploitation of more SAR missions, the developed dataset, composed of Sentinel-1, SAOCOM and COSMO-SkyMed images, is comprehensive of multiple images gathered over the same area with short time lag (below 15 minutes). Finally, the diversified processing chains and the results for each mission product and scenarios are discussed. Being the first dataset of SLC (Single Look Complex) SAR multi-frequency data, the present work intends to encourage additional research in this promising field of research.
NOAA/NESDIS a initié le programme “Alaska SAR Demonstration” dont l'objectif est de faire la démonstration du potentiel des images RSO en bande C de RADARSAT-1 à fournir une information utile et en temps opportun sur l'environnement et pour la gestion des ressources pour des utilisateurs en Alaska. Un des produits développés dans le cadre du programme est une liste de localisations des navires. Cet article décrit l'algorithme développé pour générer ce produit par le biais de la détection automatique des navires basée sur des changements dans les statistiques locales. À l'aide d'images à basse résolution (100 mètres d'espacement), on démontre que l'on peut détecter des navires de dimension supérieure à 35 mètres (représentant 105 navires sur un total de 272 dans la zone test) avec un taux de fausse alerte de 0,01% pour une seule détection. Avec des images à haute résolution (50 mètres d'espacement), on peut détecter des navires d'une dimension supérieure à 32 mètres (représentant 124 navires sur 272) avec un taux de fausse alerte de 0,002% pour une seule détection. L'algorithme est entièrement automatisé et prend environ 10 minutes de temps-machine pour traiter une image ScanSAR en mode B large.
The goal of the National Oceanic and Atmospheric Administration (NOAA) CoastWatch Program is to provide satellite and other environmental data and products for near-real-time monitoring of U.S. coastal waters in support of environmental science, management, and hazard response. During the last few years, products available through CoastWatch have expanded beyond the original infrared, visible, and sea surface temperature images to include ocean color, scatterometer wind, and synthetic aperture radar (SAR) images. A NOAA research and development program with partners in government, academia, and industry has endeavored to develop coastal ocean SAR applications for CoastWatch. Some of these applications, in particular wind measurement and hard target (i.e., vessel) detection, were developed for a preoperational demonstration. Users include the NOAA National Weather Service, the Alaska Department of Fish and Game, and the U.S. Coast Guard.
1 The Regional Information Report Series was established in 1987 to provide an information access system for all unpublished division reports. These reports frequently serve diverse ad hoc informational purposes or archive basic uninterpreted data. To accommodate timely reporting of recently collected information, reports in this series undergo only limited internal review and may contain preliminary data; this information may be subsequently finalized and published in the formal literature. Consequently, these reports should not be cited without prior approval of the author or the Division of Commercial Fisheries.
The National Oeanic and Atmospheric Administration (NOAA)/National
Environmental Satellite, Data, And Information Service (NESDIS) is in
the second year of a two-year demonstration of Synthetic Aperture Radar
(SAR) derived products called the Alaska SAR Demonstration (AKDEMO).
This demonstration provides near real-time SAR data and derived
products, including wind images and vectors, hard target locations,
along with ancillary data, to specific users in the government
community. One of the derived products are vessel positions obtained
from a constant false alarm rate (CFAR) vessel detection algorithm
developed by Veridian ERIM. This algorithm has been tested and validated
to maximize the number of ships found while minimizing the number or
false alarms on one SAR image of the Red King Crab fishery in Bristol
Bay on October 18, 1999. This resulted in using a detection statistic
threshold of about 5.5, depending on image resolution used. Until now,
this validation has been done with only general knowledge of fishing
fleet size and location, but no in situ vessel information. This paper
presents the results of a validation of the SAR vessel detection
algorithm using observer reported vessel positions along with
information on vessel size and local wind speed
Traditional Coast Guard Duties Suffer, Study Says
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Validation of an automatic vessel detection algorithm using in-situ positions
- K S Friedman
- C Wackerman
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- W G Pichel
- P Clemente-Colón
- X Li
Friedman, K. S., C. Wackerman, F. Funk, K. Rowell,
W. G. Pichel, P. Clemente-Colón, and X. Li, 2000,
Validation of an automatic vessel detection
algorithm using in-situ positions. Proceedings
IGARSS 2000, 24-28 July 2000, Honolulu, Hawaii.
- W Pichel
- P Clemente-Colón
Pichel, W. and P. Clemente-Colón, 2000, NOAA
CoastWatch SAR Applications and Demonstration,
Johns Hopkins APL Technical Digest, 21(1), 49-57.
Fishery Information Packet for the Bristol Bay Red King Crab Fishery 2002Regional Information Report No. 4K02–45
- K Granath
Validation of a CFAR Vessel Detection Algorithm Using Known Vessel Locations
- K S Friedman
- C Wackerman
- F Funk
- W G Pichel
- P Clemente-Colon
- X Li
Friedman, K. S., C. Wackerman, F. Funk, W. G.
Pichel, P. Clemente-Colon, and X. Li, 2001,
Validation of a CFAR Vessel Detection Algorithm
Using Known Vessel Locations.
Proceedings
IGARSS 2001, July 2001, Sydney, Australia.