Name
Abstract | ||
|---|---|---|
We examine the performance of an automated sea ice classification algorithm based on TerraSAR-X ScanSAR data. In the first step of our process chain, gray-level co-occurrence matrix(GLCM)-based texture features are extracted from the image. In the second step, these data are fed into an artificial neural network to classify each pixel. Performance of our implementation is examined by utilizing a time series of ScanSAR images in the Western Barents Sea, acquired in spring 2013. The network is trained on the initial image of the time series and then applied to subsequent images. We obtain a reasonable classification accuracy of at least 70% depending on the choice of our ice-type regime, when the incidence angle range of the training data matches that of the classified image. Computational cost of our approach is sufficiently moderate to consider this classification procedure a promising step toward operational, near-realtime ice charting. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/JSTARS.2015.2436993 | Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of |
Keywords | Field | DocType |
earth and atmospheric sciences,pattern analysis,remote sensing,texture,sea ice,accuracy,time series analysis,image classification,feature extraction,artificial neural network,neural networks,synthetic aperture radar | Computer vision,Time series,Sea ice,Synthetic aperture radar,Matrix (mathematics),Remote sensing,Feature extraction,Artificial intelligence,Pixel,Artificial neural network,Mathematics,X band | Journal |
Volume | Issue | ISSN |
PP | 99 | 1939-1404 |
Citations | PageRank | References |
7 | 0.53 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ressel, R. | 1 | 12 | 3.43 |
| Frost, A. | 2 | 10 | 1.99 |
| Lehner, S. | 3 | 7 | 0.53 |