First tests on near real time ice type classification in Antarctica - Lehner, Susanne; Krumpen, Thomas; Frost, Anja; Ressel, Rudolf; Busche, Thomas E.; Schwarz, Egbert (International Geoscience and Remote Sensing Symposium (IGARSS), 2014)
In this paper, we explore the capabilities of an algorithm for ice type classification. Our main motivation and exemplary application was the recent incident of the research vessel Akademik Shokalskiy, which was trapped in pack ice for about two weeks. Strong winds had driven ice floes into a bay, forming an area of pack ice, blocking the ship's advancement. High-resolution satellite images helped to assess the ice conditions at the location. To extract relevant information automatically from the images, we apply an algorithm that is aimed to generate an ice chart, outlining the different ice type zones such as pack ice, fast ice, open water. The algorithm is based on texture analysis. Textures are selected that allow recognition of different structures in ice. Subsequently, a neural network performs the classification. Since results are output in near real time, the algorithm offers new opportunities for ship routing in ice infested areas.
CRIS-Team Scopus Importer:2024-03-18
Keywords: GLCM features; neural network; sea ice classification; ship navigation; TerraSAR-X