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RADARSAT SAR for oil spill response
Institution:1. State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of the Chinese Academy of Sciences, Beijing 100875, China;2. Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China;3. VITROCISET, Bratustrasse 7, 64293 Darmstadt, Germany;4. School for the Environment, Earth and Ocean Sciences, University of Massachusetts, Boston, MA, USA;5. Laboratoire des Sciences du Climat et de l''Environnement, CEA-CNRS-UVSQ, 91191 Gif sur Yvette, France;6. College of Water Sciences, Beijing Normal University, Beijing 100875, China;7. Terrestrial Information Systems Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA;8. Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA;9. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;10. Institute of Environmental Physics, University of Bremen, Otto-Hahn-Allee 1, 28359 Bremen, Germany;1. State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China;2. Division of Landscape Architecture, Faculty of Architecture, The University of Hong Kong, Pokfulam, Hong Kong, China;3. CESBIO - Centre d''Etudes Spatiales de la BIOsphère, CESBIO UMR 5126, 31401 Toulouse, France;4. Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China;5. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China;1. State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China;2. Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875;3. Laboratoire des Sciences du Climat et de l''Environnement, CEA-CNRS-UVSQ, 91191 Gif sur Yvette, France;4. School for the Environment, University of Massachusetts Boston, Boston, MA, USA;5. Terrestrial Information Systems Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA;6. Earth System Science Interdisciplinary Center, University of Maryland College Park, MD, USA
Abstract:RADARSAT synthetic aperture radar imagery has been successfully classified to delineate oil slicks on water using training areas for various degrees of oil coverage located within each image. Three and four class schemes have been tested with imagery from the Nakhodka and Milford Haven spills. An interactive graphical editor has been developed using the classified images to re-initialize the SPILLSIM oil spill model during a simulation.
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