NDLI: Unsupervised segmentation of synthetic aperture radar sea ice imagery using MRF models

Content Provider	IEEE Xplore Digital Library
Author	Huawu Deng Clausi, D.A.
Copyright Year	2004
Description	Author affiliation: University of Waterloo (Huawu Deng)
Abstract	Due to both environmental and sensor reasons, it is challenging to develop computer-assisted algorithms to segment SAR (synthetic aperture radar) sea ice imagery. In this research, images containing either ice and water or multiple ice classes are segmented. This paper proposes to use the image intensity to discriminate ice from water and to use texture features to separate different ice types. In order to seamlessly combine spatial relationship information in an ice image with various image features, a novel Bayesian segmentation approach is developed. Experiments demonstrate that the proposed algorithm is able to segment both types of sea ice images and achieves an improvement over the standard MRF (Markov random field) based method, the finite Gamma mixture model and the K-means clustering method.
Starting Page	43
Ending Page	50
File Size	2109764
Page Count	8
File Format	PDF
ISBN	0769521274
DOI	10.1109/CCCRV.2004.1301420
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-05-17
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image sensors Image segmentation Design engineering Clustering methods Bayesian methods Systems engineering and theory Sensor systems Synthetic aperture radar Sea ice Markov random fields
Content Type	Text
Resource Type	Article

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