SAR image despeckling by employment of multiwavelet based Hidden Markov Model

Wen Long Song; Dong Seog Han

doi:10.1109/URSIAP-RASC.2016.7601161

SAR image despeckling by employment of multiwavelet based Hidden Markov Model

Wen Long Song, Dong Seog Han

School of Electronics Engineering

Kyungpook National University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Despite the high resolution of synthetic aperture radar (SAR) images, speckle noise remains a big challenge in the image. A novel method of restraining speckle noise in a SAR image by use of the multiwavelet based Hidden Markov Model (HMM) is proposed in this paper. In order to estimate the HMM parameters, the expectation maximization (EM) algorithm is adopted. The experimental results show that the proposed despeckling method in this paper improves the image quality and increases the equivalent number of looks (ENL) while keeping more edge information compared to the single wavelet multiscale based HMM.

Original language	English
Title of host publication	2016 URSI Asia-Pacific Radio Science Conference, URSI AP-RASC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	938-941
Number of pages	4
ISBN (Electronic)	9781467388016
DOIs	https://doi.org/10.1109/URSIAP-RASC.2016.7601161
State	Published - 19 Oct 2016
Event	2016 URSI Asia-Pacific Radio Science Conference, URSI AP-RASC 2016 - Seoul, Korea, Republic of Duration: 21 Aug 2016 → 25 Aug 2016

Publication series

Name	2016 URSI Asia-Pacific Radio Science Conference, URSI AP-RASC 2016

Conference

Conference	2016 URSI Asia-Pacific Radio Science Conference, URSI AP-RASC 2016
Country/Territory	Korea, Republic of
City	Seoul
Period	21/08/16 → 25/08/16

Keywords

Bayes minimum mean square error (MMSE)
Discrete multiwavelet transform (DMWT)
Expectation maximization (EM)
Hidden markov model (HMM)
Median of absolute deviation (MAD)

Access to Document

10.1109/URSIAP-RASC.2016.7601161

Cite this

Song, W. L., & Han, D. S. (2016). SAR image despeckling by employment of multiwavelet based Hidden Markov Model. In 2016 URSI Asia-Pacific Radio Science Conference, URSI AP-RASC 2016 (pp. 938-941). Article 7601161 (2016 URSI Asia-Pacific Radio Science Conference, URSI AP-RASC 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/URSIAP-RASC.2016.7601161

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title = "SAR image despeckling by employment of multiwavelet based Hidden Markov Model",

abstract = "Despite the high resolution of synthetic aperture radar (SAR) images, speckle noise remains a big challenge in the image. A novel method of restraining speckle noise in a SAR image by use of the multiwavelet based Hidden Markov Model (HMM) is proposed in this paper. In order to estimate the HMM parameters, the expectation maximization (EM) algorithm is adopted. The experimental results show that the proposed despeckling method in this paper improves the image quality and increases the equivalent number of looks (ENL) while keeping more edge information compared to the single wavelet multiscale based HMM.",

keywords = "Bayes minimum mean square error (MMSE), Discrete multiwavelet transform (DMWT), Expectation maximization (EM), Hidden markov model (HMM), Median of absolute deviation (MAD)",

author = "Song, {Wen Long} and Han, {Dong Seog}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 URSI Asia-Pacific Radio Science Conference, URSI AP-RASC 2016 ; Conference date: 21-08-2016 Through 25-08-2016",

year = "2016",

month = oct,

day = "19",

doi = "10.1109/URSIAP-RASC.2016.7601161",

language = "English",

series = "2016 URSI Asia-Pacific Radio Science Conference, URSI AP-RASC 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Song, WL & Han, DS 2016, SAR image despeckling by employment of multiwavelet based Hidden Markov Model. in 2016 URSI Asia-Pacific Radio Science Conference, URSI AP-RASC 2016., 7601161, 2016 URSI Asia-Pacific Radio Science Conference, URSI AP-RASC 2016, Institute of Electrical and Electronics Engineers Inc., pp. 938-941, 2016 URSI Asia-Pacific Radio Science Conference, URSI AP-RASC 2016, Seoul, Korea, Republic of, 21/08/16. https://doi.org/10.1109/URSIAP-RASC.2016.7601161

SAR image despeckling by employment of multiwavelet based Hidden Markov Model. / Song, Wen Long; Han, Dong Seog.
2016 URSI Asia-Pacific Radio Science Conference, URSI AP-RASC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 938-941 7601161 (2016 URSI Asia-Pacific Radio Science Conference, URSI AP-RASC 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - SAR image despeckling by employment of multiwavelet based Hidden Markov Model

AU - Song, Wen Long

AU - Han, Dong Seog

PY - 2016/10/19

Y1 - 2016/10/19

N2 - Despite the high resolution of synthetic aperture radar (SAR) images, speckle noise remains a big challenge in the image. A novel method of restraining speckle noise in a SAR image by use of the multiwavelet based Hidden Markov Model (HMM) is proposed in this paper. In order to estimate the HMM parameters, the expectation maximization (EM) algorithm is adopted. The experimental results show that the proposed despeckling method in this paper improves the image quality and increases the equivalent number of looks (ENL) while keeping more edge information compared to the single wavelet multiscale based HMM.

AB - Despite the high resolution of synthetic aperture radar (SAR) images, speckle noise remains a big challenge in the image. A novel method of restraining speckle noise in a SAR image by use of the multiwavelet based Hidden Markov Model (HMM) is proposed in this paper. In order to estimate the HMM parameters, the expectation maximization (EM) algorithm is adopted. The experimental results show that the proposed despeckling method in this paper improves the image quality and increases the equivalent number of looks (ENL) while keeping more edge information compared to the single wavelet multiscale based HMM.

KW - Bayes minimum mean square error (MMSE)

KW - Discrete multiwavelet transform (DMWT)

KW - Expectation maximization (EM)

KW - Hidden markov model (HMM)

KW - Median of absolute deviation (MAD)

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M3 - Conference contribution

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SP - 938

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BT - 2016 URSI Asia-Pacific Radio Science Conference, URSI AP-RASC 2016

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Y2 - 21 August 2016 through 25 August 2016

ER -

SAR image despeckling by employment of multiwavelet based Hidden Markov Model

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