Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IGARSS.2014.6947587 | Geoscience and Remote Sensing Symposium |
Keywords | Field | DocType |
feature extraction,geophysical image processing,geophysical techniques,image classification,neural nets,remote sensing,Akademik Shokalskiy,Antarctica,high-resolution satellite images,ice conditions,ice floes,ice type zones,near real time ice type classification,neural network,pack ice area,research vessel,texture analysis,GLCM features,TerraSAR-X,neural network,sea ice classification,ship navigation | Sea ice,Satellite,Computer science,Remote sensing,Ice type,Real-time computing,Fast ice,Chart,Artificial neural network,Arctic ice pack,Open water | Conference |
ISSN | Citations | PageRank |
2153-6996 | 1 | 0.37 |
References | Authors | |
3 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Susanne Lehner | 1 | 116 | 15.23 |
| Krumpen, T. | 2 | 2 | 0.78 |
| Frost, A. | 3 | 10 | 1.99 |
| Ressel, R. | 4 | 12 | 3.43 |