Markov Chain Monte Carlo Sampling on Polar Sea Ice Classification
The Remund-Long (RL) Multi-Sensor Sea Ice Classification algorithm� combines both radiometer and scatterometer data using Principle Component Analysis and reduces the dimensionality and noise level of the data. The algorithm uses an iterative Maximum a Posteriori (MAP) method based on a multi-varian...
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ftutahsudc:oai:digitalcommons.usu.edu:spacegrant-1228 2023-05-15T13:41:20+02:00 Markov Chain Monte Carlo Sampling on Polar Sea Ice Classification Li, Shuyi 2004-05-10T16:00:00Z application/pdf https://digitalcommons.usu.edu/spacegrant/2004/2004/37 https://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=1228&context=spacegrant unknown DigitalCommons@USU https://digitalcommons.usu.edu/spacegrant/2004/2004/37 https://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=1228&context=spacegrant Utah Space Grant Consortium text 2004 ftutahsudc 2022-03-07T20:37:53Z The Remund-Long (RL) Multi-Sensor Sea Ice Classification algorithm� combines both radiometer and scatterometer data using Principle Component Analysis and reduces the dimensionality and noise level of the data. The algorithm uses an iterative Maximum a Posteriori (MAP) method based on a multi-variant Gaussian model with a temporal prior. As a result, the algorithm successfully classifies Winter Antarctic region into five different ice types. However, due to the nature of this pixel wise classification algorithm, the final classification is more likely to be corrupted by slat-pepper-shaped artifacts. Such artifacts are introduced by the Scatterometer Image Reconstruction (SIR) algorithm which utilizes multi-swath raw scatterometer data to generate high resolution images. In order to resolve such problem in RL algorithm, posterior distribution function with spatial prior is embedded into the classification process. A Markov Chain Monte Carlo (MCMC) sampling method is one way to sample such posterior distribution of the state space in which each element of the space has the size of an entire image. This report gives a brief introduction to the concept of Metropolis-Hastings Markov Chain Monte Carlo (MH MCMC) algorithm, discusses its implementation on polar sea ice classification, and compares the result with the RL algorithm. Text Antarc* Antarctic Sea ice Utah State University: DigitalCommons@USU Antarctic Hastings ENVELOPE(-154.167,-154.167,-85.567,-85.567) |
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Utah State University: DigitalCommons@USU |
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The Remund-Long (RL) Multi-Sensor Sea Ice Classification algorithm� combines both radiometer and scatterometer data using Principle Component Analysis and reduces the dimensionality and noise level of the data. The algorithm uses an iterative Maximum a Posteriori (MAP) method based on a multi-variant Gaussian model with a temporal prior. As a result, the algorithm successfully classifies Winter Antarctic region into five different ice types. However, due to the nature of this pixel wise classification algorithm, the final classification is more likely to be corrupted by slat-pepper-shaped artifacts. Such artifacts are introduced by the Scatterometer Image Reconstruction (SIR) algorithm which utilizes multi-swath raw scatterometer data to generate high resolution images. In order to resolve such problem in RL algorithm, posterior distribution function with spatial prior is embedded into the classification process. A Markov Chain Monte Carlo (MCMC) sampling method is one way to sample such posterior distribution of the state space in which each element of the space has the size of an entire image. This report gives a brief introduction to the concept of Metropolis-Hastings Markov Chain Monte Carlo (MH MCMC) algorithm, discusses its implementation on polar sea ice classification, and compares the result with the RL algorithm. |
format |
Text |
author |
Li, Shuyi |
spellingShingle |
Li, Shuyi Markov Chain Monte Carlo Sampling on Polar Sea Ice Classification |
author_facet |
Li, Shuyi |
author_sort |
Li, Shuyi |
title |
Markov Chain Monte Carlo Sampling on Polar Sea Ice Classification |
title_short |
Markov Chain Monte Carlo Sampling on Polar Sea Ice Classification |
title_full |
Markov Chain Monte Carlo Sampling on Polar Sea Ice Classification |
title_fullStr |
Markov Chain Monte Carlo Sampling on Polar Sea Ice Classification |
title_full_unstemmed |
Markov Chain Monte Carlo Sampling on Polar Sea Ice Classification |
title_sort |
markov chain monte carlo sampling on polar sea ice classification |
publisher |
DigitalCommons@USU |
publishDate |
2004 |
url |
https://digitalcommons.usu.edu/spacegrant/2004/2004/37 https://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=1228&context=spacegrant |
long_lat |
ENVELOPE(-154.167,-154.167,-85.567,-85.567) |
geographic |
Antarctic Hastings |
geographic_facet |
Antarctic Hastings |
genre |
Antarc* Antarctic Sea ice |
genre_facet |
Antarc* Antarctic Sea ice |
op_source |
Utah Space Grant Consortium |
op_relation |
https://digitalcommons.usu.edu/spacegrant/2004/2004/37 https://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=1228&context=spacegrant |
_version_ |
1766149484842057728 |