Application of neural networks for sea ice classification in polarimetric SAR images
- Massachusetts Inst. of Tech., Cambridge, MA (United States)
- California Inst. of Tech., Pasadena, CA (United States). Jet Propulsion Lab.
Classification of sea ice types using polarimetric radar is an area of considerable current interest and research. Several automatic methods have been developed to classify sea ice types from fully polarimetric synthetic aperture radar (SAR) images, and these techniques are generally grouped into supervised and unsupervised approaches. In previous work, supervised methods have been shown to yield higher accuracy than unsupervised techniques, but suffer from the need for human interaction to determine classes and training regions. In contrast, unsupervised methods determine classes automatically, but generally show limited ability to accurately divide terrain into natural classes. In this paper, a new classification technique is applied to determine sea ice types in polarimetric and multifrequency SAR images, utilizing an unsupervised neural network to provide automatic classification, and employing an iterative algorithm to improve the performance.
- OSTI ID:
- 137129
- Journal Information:
- IEEE Transactions on Geoscience and Remote Sensing, Journal Name: IEEE Transactions on Geoscience and Remote Sensing Journal Issue: 3 Vol. 33; ISSN IGRSD2; ISSN 0196-2892
- Country of Publication:
- United States
- Language:
- English
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