NAVIGATION ASSISTANCE FOR ICE-INFESTED WATERS THROUGH AUTOMATIC ICEBERG DETECTION AND ICE CLASSIFICATION BASED ON TERRASAR-X IMAGERY
- German Aerospace Center (DLR), Earth Observation Center, Remote Sensing Technology Institute, 28199 Bremen, Germany
Keywords: CFAR detector, GLCM features, iceberg monitoring, neural network, sea ice classification, TerraSAR-X
Abstract. Most icebergs present in northern latitudes originate from western Greenland glaciers, from where they drift into Baffin Bay, circulating north along Greenland coast and south along Canadian coast. Some of them drift more southwards up to Newfoundland, where they frequently cross shipping routes. Furthermore, the Arctic summer sea ice coverage significantly decreased over the last three decades. This has attracted numerous attentions from maritime end-users. To keep Arctic shipping routes safe, the monitoring of sea ice and icebergs is crucial. For this purpose, satellite-based Synthetic Aperture Radar (SAR) is well suited. Equipped with an active radar antenna, SAR satellites provide image data of the ocean and frozen waters independent of weather conditions, cloud cover or absence of daylight. In this paper, we present a processor for sea ice classification and (subsequent) iceberg detection based on TerraSAR-X imagery. In the classification step, texture features are extracted from the images and fed into a neural network, indicating areas of low sea ice concentration. Then, an adapted Constant False Alarm Rate (CFAR) detector is executed in order to detect icebergs. In the end, sea ice boundary and iceberg positions are output. Our experiments deal with HH polarized TerraSAR-X images taken in spring season in the Baffin Bay off the western Greenland coast, where both, sea ice and icebergs are present. Our results exemplify how a comprehensive ice processor with complementary information can be set up for near real time (NRT) service in ice infested waters.