ETVOS: An enhanced total variation optimization segmentation approach for SAR sea-ice image segmentation
This paper presents a novel enhanced total variation optimization segmentation (ETVOS) approach consisting of two phases to segmentation of various sea-ice types. In the total variation optimization phase, the Rudin-Osher-Fatemi total variation model was modified and implemented iteratively to estim...
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ftxiamenuniv:oai:dspace.xmu.edu.cn:2288/92591 2023-05-15T18:17:26+02:00 ETVOS: An enhanced total variation optimization segmentation approach for SAR sea-ice image segmentation Kwon, Tae-Jung Li, Jonathan Wong, Alexander 李军 李茂青 2013 http://dspace.xmu.edu.cn/handle/2288/92591 en_US eng Institute of Electrical and Electronics Engineers Inc. IEEE Transactions on Geoscience and Remote Sensing, 2013,51(2):925-934 0196-2892 20130515969659 http://dspace.xmu.edu.cn/handle/2288/92591 http://dx.doi.org/10.1109/TGRS.2012.2205259 Adaptive systems Estimation Image segmentation Iterative methods Optimization Sea ice Article 2013 ftxiamenuniv 2020-07-21T11:45:55Z This paper presents a novel enhanced total variation optimization segmentation (ETVOS) approach consisting of two phases to segmentation of various sea-ice types. In the total variation optimization phase, the Rudin-Osher-Fatemi total variation model was modified and implemented iteratively to estimate the piecewise constant state from a nonpiecewise constant state (the original noisy imagery) by minimizing the total variation constraints. In the finite mixture model classification phase, based on the pixel distribution, an expectation maximization method was performed to estimate the final class likelihood using a Gaussian mixture model. Then, a maximum likelihood classification technique was utilized to estimate the final class of each pixel that appeared in the product of the total variation optimization phase. The proposed method was tested on a synthetic image and various subsets of RADARSAT-2 imagery, and the results were compared with other well-established approaches. With the advantage of a short processing time, the visual inspection and quantitative analysis of segmentation results confirm the superiority of the proposed ETVOS method over other existing methods. ? 1980-2012 IEEE. Article in Journal/Newspaper Sea ice Xiamen University Institutional Repository |
institution |
Open Polar |
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Xiamen University Institutional Repository |
op_collection_id |
ftxiamenuniv |
language |
English |
topic |
Adaptive systems Estimation Image segmentation Iterative methods Optimization Sea ice |
spellingShingle |
Adaptive systems Estimation Image segmentation Iterative methods Optimization Sea ice Kwon, Tae-Jung Li, Jonathan Wong, Alexander 李军 李茂青 ETVOS: An enhanced total variation optimization segmentation approach for SAR sea-ice image segmentation |
topic_facet |
Adaptive systems Estimation Image segmentation Iterative methods Optimization Sea ice |
description |
This paper presents a novel enhanced total variation optimization segmentation (ETVOS) approach consisting of two phases to segmentation of various sea-ice types. In the total variation optimization phase, the Rudin-Osher-Fatemi total variation model was modified and implemented iteratively to estimate the piecewise constant state from a nonpiecewise constant state (the original noisy imagery) by minimizing the total variation constraints. In the finite mixture model classification phase, based on the pixel distribution, an expectation maximization method was performed to estimate the final class likelihood using a Gaussian mixture model. Then, a maximum likelihood classification technique was utilized to estimate the final class of each pixel that appeared in the product of the total variation optimization phase. The proposed method was tested on a synthetic image and various subsets of RADARSAT-2 imagery, and the results were compared with other well-established approaches. With the advantage of a short processing time, the visual inspection and quantitative analysis of segmentation results confirm the superiority of the proposed ETVOS method over other existing methods. ? 1980-2012 IEEE. |
format |
Article in Journal/Newspaper |
author |
Kwon, Tae-Jung Li, Jonathan Wong, Alexander 李军 李茂青 |
author_facet |
Kwon, Tae-Jung Li, Jonathan Wong, Alexander 李军 李茂青 |
author_sort |
Kwon, Tae-Jung |
title |
ETVOS: An enhanced total variation optimization segmentation approach for SAR sea-ice image segmentation |
title_short |
ETVOS: An enhanced total variation optimization segmentation approach for SAR sea-ice image segmentation |
title_full |
ETVOS: An enhanced total variation optimization segmentation approach for SAR sea-ice image segmentation |
title_fullStr |
ETVOS: An enhanced total variation optimization segmentation approach for SAR sea-ice image segmentation |
title_full_unstemmed |
ETVOS: An enhanced total variation optimization segmentation approach for SAR sea-ice image segmentation |
title_sort |
etvos: an enhanced total variation optimization segmentation approach for sar sea-ice image segmentation |
publisher |
Institute of Electrical and Electronics Engineers Inc. |
publishDate |
2013 |
url |
http://dspace.xmu.edu.cn/handle/2288/92591 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
http://dx.doi.org/10.1109/TGRS.2012.2205259 |
op_relation |
IEEE Transactions on Geoscience and Remote Sensing, 2013,51(2):925-934 0196-2892 20130515969659 http://dspace.xmu.edu.cn/handle/2288/92591 |
_version_ |
1766191655883374592 |