Name
Abstract | ||
|---|---|---|
We have studied the feature extraction from sea ice SAR images based on non-negative factorization methods. The methods reported here are the sparseness-constrained non-negative matrix factorization (SC-NMF) and Non-negative tensor factorization (NTF). The studies performed show that these methods can be used to extract meaningful features from SAR images and that they can be used in sea ice SAR classification. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/IGARSS.2008.4779917 | Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International |
Keywords | Field | DocType |
feature extraction,image classification,remote sensing by radar,sea ice,synthetic aperture radar,Baltic Sea,Gulf of Finland,NTF,Non-negative Tensor Factorization,SC-NMF,Sparseness-Constrained Non- negative Matrix Factorization,edge classification,feature extraction,image classification,non-negative factorization method,sea ice SAR image,sea ice classification algorithm,synthetic aperture radar,Classification,Feature Extraction,NMF,NTF,SAR,Sea Ice | Sea ice,Synthetic aperture radar,Computer science,Remote sensing,Artificial intelligence,Contextual image classification,Sparse matrix,Computer vision,Pattern recognition,Matrix decomposition,Feature extraction,Factorization,Non-negative matrix factorization | Conference |
Volume | ISBN | Citations |
4 | 978-1-4244-2808-3 | 1 |
PageRank | References | Authors |
0.36 | 7 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juha Karvonen | 1 | 11 | 1.26 |
| Arto Kaarna | 2 | 174 | 27.50 |