Details
| Original language | English |
|---|---|
| Pages (from-to) | 64-78 |
| Number of pages | 15 |
| Journal | Advances in Space Research |
| Volume | 64 |
| Issue number | 1 |
| Early online date | 19 Mar 2019 |
| Publication status | Published - 1 Jul 2019 |
Abstract
Synthetic Aperture Radar (SAR) data have gained interest for a variety of remote sensing applications, given the capability of SAR sensors to operate independent of solar radiation and day/night conditions. However, the radiometric quality of SAR images is hindered by speckle noise, which affects further image processing and interpretation. As such, speckle reduction is a crucial pre-processing step in many remote sensing studies based on SAR imagery. This study proposes a new adaptive de-speckling method based on a Gaussian Markov Random Field (GMRF) model. The proposed method integrates both pixel-wised and contextual information using a weighted summation technique. As a by-product of the proposed method, a de-speckled pseudo-span image, which is obtained from the least-squares analysis of the de-speckled multi-polarization channels, is also produced. Experimental results from the medium resolution, fully polarimetric L-band ALOS PALSAR data demonstrate the effectiveness of the proposed algorithm compared to other well-known de-speckling approaches. The de-speckled images produced by the proposed method maintainthe mean value of the original image in homogenous areas, while preserving the edges of features in heterogeneous regions. In particular, the equivalent number of look (ENL) achieved using the proposed method improves by about 15% and 47% compared to the NL-SAR and SARBM3D de-speckling approaches, respectively. Other evaluation indices, such as the mean and variance of the ratio image also reveal the superiority of the proposed method relative to other de-speckling approaches examined in this study.
Keywords
- ALOS PALSAR, Contextual analysis, De-speckling, Gaussian, Markov random field (MRF), Synthetic Aperture Radar (SAR)
ASJC Scopus subject areas
- Engineering(all)
- Aerospace Engineering
- Physics and Astronomy(all)
- Astronomy and Astrophysics
- Earth and Planetary Sciences(all)
- Geophysics
- Earth and Planetary Sciences(all)
- Atmospheric Science
- Earth and Planetary Sciences(all)
- Space and Planetary Science
- Earth and Planetary Sciences(all)
- General Earth and Planetary Sciences
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In: Advances in Space Research, Vol. 64, No. 1, 01.07.2019, p. 64-78.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - A Gaussian random field model for de-speckling of multi-polarized Synthetic Aperture Radar data
AU - Mahdianpari, Masoud
AU - Motagh, Mahdi
AU - Akbari, Vahid
AU - Mohammadimanesh, Fariba
AU - Salehi, Bahram
PY - 2019/7/1
Y1 - 2019/7/1
N2 - Synthetic Aperture Radar (SAR) data have gained interest for a variety of remote sensing applications, given the capability of SAR sensors to operate independent of solar radiation and day/night conditions. However, the radiometric quality of SAR images is hindered by speckle noise, which affects further image processing and interpretation. As such, speckle reduction is a crucial pre-processing step in many remote sensing studies based on SAR imagery. This study proposes a new adaptive de-speckling method based on a Gaussian Markov Random Field (GMRF) model. The proposed method integrates both pixel-wised and contextual information using a weighted summation technique. As a by-product of the proposed method, a de-speckled pseudo-span image, which is obtained from the least-squares analysis of the de-speckled multi-polarization channels, is also produced. Experimental results from the medium resolution, fully polarimetric L-band ALOS PALSAR data demonstrate the effectiveness of the proposed algorithm compared to other well-known de-speckling approaches. The de-speckled images produced by the proposed method maintainthe mean value of the original image in homogenous areas, while preserving the edges of features in heterogeneous regions. In particular, the equivalent number of look (ENL) achieved using the proposed method improves by about 15% and 47% compared to the NL-SAR and SARBM3D de-speckling approaches, respectively. Other evaluation indices, such as the mean and variance of the ratio image also reveal the superiority of the proposed method relative to other de-speckling approaches examined in this study.
AB - Synthetic Aperture Radar (SAR) data have gained interest for a variety of remote sensing applications, given the capability of SAR sensors to operate independent of solar radiation and day/night conditions. However, the radiometric quality of SAR images is hindered by speckle noise, which affects further image processing and interpretation. As such, speckle reduction is a crucial pre-processing step in many remote sensing studies based on SAR imagery. This study proposes a new adaptive de-speckling method based on a Gaussian Markov Random Field (GMRF) model. The proposed method integrates both pixel-wised and contextual information using a weighted summation technique. As a by-product of the proposed method, a de-speckled pseudo-span image, which is obtained from the least-squares analysis of the de-speckled multi-polarization channels, is also produced. Experimental results from the medium resolution, fully polarimetric L-band ALOS PALSAR data demonstrate the effectiveness of the proposed algorithm compared to other well-known de-speckling approaches. The de-speckled images produced by the proposed method maintainthe mean value of the original image in homogenous areas, while preserving the edges of features in heterogeneous regions. In particular, the equivalent number of look (ENL) achieved using the proposed method improves by about 15% and 47% compared to the NL-SAR and SARBM3D de-speckling approaches, respectively. Other evaluation indices, such as the mean and variance of the ratio image also reveal the superiority of the proposed method relative to other de-speckling approaches examined in this study.
KW - ALOS PALSAR
KW - Contextual analysis
KW - De-speckling
KW - Gaussian
KW - Markov random field (MRF)
KW - Synthetic Aperture Radar (SAR)
UR - http://www.scopus.com/inward/record.url?scp=85063374238&partnerID=8YFLogxK
U2 - 10.1016/j.asr.2019.03.013
DO - 10.1016/j.asr.2019.03.013
M3 - Article
AN - SCOPUS:85063374238
VL - 64
SP - 64
EP - 78
JO - Advances in Space Research
JF - Advances in Space Research
SN - 0273-1177
IS - 1
ER -