Title :
Sea Ice SAR Feature Extraction by Non-Negative Matrix and Tensor Factorization
Author :
Karvonen, Juha ; Kaarna, Arto
Author_Institution :
Finnish Instit. of Marine Res., Helsinki
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.
Keywords :
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; Data mining; Error correction; Feature extraction; Image reconstruction; Independent component analysis; Marine technology; Navigation; Sea ice; Sparse matrices; Tensile stress; Classification; Feature Extraction; NMF; NTF; SAR; Sea Ice;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International
Conference_Location :
Boston, MA
Print_ISBN :
978-1-4244-2807-6
Electronic_ISBN :
978-1-4244-2808-3
DOI :
10.1109/IGARSS.2008.4779917
