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An Analysis of Black Fill Artefacts Noise Removal on GRD Products Sentinel-1 Data

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@article{IJASEIT8079,
   author = {Haris Suka Dyatmika and Katmoko Ari Sambodo and Rahmat Arief},
   title = {An Analysis of Black Fill Artefacts Noise Removal on GRD Products Sentinel-1 Data},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {9},
   number = {1},
   year = {2019},
   pages = {274--280},
   keywords = {sentinel-1; Synthetic Aperture Radar (SAR); noise removal; ground range detected; border noise.},
   abstract = {

Synthetic Aperture Radar (SAR) is an active remote sensing satellite which is able to acquire cloud free images in all weather conditions. It is also capable of night time operation. Sentinel-1 data is one of SAR data which is good for monitoring natural resources in area with high cloud cover throughout the year. Processing the data until mosaic product needs good methods and right procedure. An highlight processes to remove noise through border of GRD data scene necessary to do because the processing chain from raw data into L1 GRD (Ground Range Detected) products were leading to artefacts at the near and far range image borders. The artefacts were not visible at a glance in the raw data but, observable clearly after performing mosaic a sets of data. Some methods to fix the problem are available to use such as common noise removal methods. This paper analysed methods to do noise removal i.e. using a tool in ESA’s provided Sentinel-1 software (Sentinel Application Platform - SNAP) and proposed noise removal method using simple thresholding and segmentation process. The mosaic products results from both method shown good results visually but the detailed histogram shown that the S-1 Remove GRD Border Noise results still have a very low value pixels in the black-fill area while the Random Noise Removal removed all of the noise. PSNR of raw data mosaic, GRD Border Noise and Random Noise Removal results sequentially 8.5, 18.6 and 19.7 dB indicated that Random Noise Removal get the highest similarity to reference data.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8079},    doi = {10.18517/ijaseit.9.1.8079} }

EndNote

%A Dyatmika, Haris Suka
%A Sambodo, Katmoko Ari
%A Arief, Rahmat
%D 2019
%T An Analysis of Black Fill Artefacts Noise Removal on GRD Products Sentinel-1 Data
%B 2019
%9 sentinel-1; Synthetic Aperture Radar (SAR); noise removal; ground range detected; border noise.
%! An Analysis of Black Fill Artefacts Noise Removal on GRD Products Sentinel-1 Data
%K sentinel-1; Synthetic Aperture Radar (SAR); noise removal; ground range detected; border noise.
%X 

Synthetic Aperture Radar (SAR) is an active remote sensing satellite which is able to acquire cloud free images in all weather conditions. It is also capable of night time operation. Sentinel-1 data is one of SAR data which is good for monitoring natural resources in area with high cloud cover throughout the year. Processing the data until mosaic product needs good methods and right procedure. An highlight processes to remove noise through border of GRD data scene necessary to do because the processing chain from raw data into L1 GRD (Ground Range Detected) products were leading to artefacts at the near and far range image borders. The artefacts were not visible at a glance in the raw data but, observable clearly after performing mosaic a sets of data. Some methods to fix the problem are available to use such as common noise removal methods. This paper analysed methods to do noise removal i.e. using a tool in ESA’s provided Sentinel-1 software (Sentinel Application Platform - SNAP) and proposed noise removal method using simple thresholding and segmentation process. The mosaic products results from both method shown good results visually but the detailed histogram shown that the S-1 Remove GRD Border Noise results still have a very low value pixels in the black-fill area while the Random Noise Removal removed all of the noise. PSNR of raw data mosaic, GRD Border Noise and Random Noise Removal results sequentially 8.5, 18.6 and 19.7 dB indicated that Random Noise Removal get the highest similarity to reference data.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8079 %R doi:10.18517/ijaseit.9.1.8079 %J International Journal on Advanced Science, Engineering and Information Technology %V 9 %N 1 %@ 2088-5334

IEEE

Haris Suka Dyatmika,Katmoko Ari Sambodo and Rahmat Arief,"An Analysis of Black Fill Artefacts Noise Removal on GRD Products Sentinel-1 Data," International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 1, pp. 274-280, 2019. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.9.1.8079.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Dyatmika, Haris Suka
AU  - Sambodo, Katmoko Ari
AU  - Arief, Rahmat
PY  - 2019
TI  - An Analysis of Black Fill Artefacts Noise Removal on GRD Products Sentinel-1 Data
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 9 (2019) No. 1
Y2  - 2019
SP  - 274
EP  - 280
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - sentinel-1; Synthetic Aperture Radar (SAR); noise removal; ground range detected; border noise.
N2  - 

Synthetic Aperture Radar (SAR) is an active remote sensing satellite which is able to acquire cloud free images in all weather conditions. It is also capable of night time operation. Sentinel-1 data is one of SAR data which is good for monitoring natural resources in area with high cloud cover throughout the year. Processing the data until mosaic product needs good methods and right procedure. An highlight processes to remove noise through border of GRD data scene necessary to do because the processing chain from raw data into L1 GRD (Ground Range Detected) products were leading to artefacts at the near and far range image borders. The artefacts were not visible at a glance in the raw data but, observable clearly after performing mosaic a sets of data. Some methods to fix the problem are available to use such as common noise removal methods. This paper analysed methods to do noise removal i.e. using a tool in ESA’s provided Sentinel-1 software (Sentinel Application Platform - SNAP) and proposed noise removal method using simple thresholding and segmentation process. The mosaic products results from both method shown good results visually but the detailed histogram shown that the S-1 Remove GRD Border Noise results still have a very low value pixels in the black-fill area while the Random Noise Removal removed all of the noise. PSNR of raw data mosaic, GRD Border Noise and Random Noise Removal results sequentially 8.5, 18.6 and 19.7 dB indicated that Random Noise Removal get the highest similarity to reference data.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8079 DO - 10.18517/ijaseit.9.1.8079

RefWorks

RT Journal Article
ID 8079
A1 Dyatmika, Haris Suka
A1 Sambodo, Katmoko Ari
A1 Arief, Rahmat
T1 An Analysis of Black Fill Artefacts Noise Removal on GRD Products Sentinel-1 Data
JF International Journal on Advanced Science, Engineering and Information Technology
VO 9
IS 1
YR 2019
SP 274
OP 280
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 sentinel-1; Synthetic Aperture Radar (SAR); noise removal; ground range detected; border noise.
AB 

Synthetic Aperture Radar (SAR) is an active remote sensing satellite which is able to acquire cloud free images in all weather conditions. It is also capable of night time operation. Sentinel-1 data is one of SAR data which is good for monitoring natural resources in area with high cloud cover throughout the year. Processing the data until mosaic product needs good methods and right procedure. An highlight processes to remove noise through border of GRD data scene necessary to do because the processing chain from raw data into L1 GRD (Ground Range Detected) products were leading to artefacts at the near and far range image borders. The artefacts were not visible at a glance in the raw data but, observable clearly after performing mosaic a sets of data. Some methods to fix the problem are available to use such as common noise removal methods. This paper analysed methods to do noise removal i.e. using a tool in ESA’s provided Sentinel-1 software (Sentinel Application Platform - SNAP) and proposed noise removal method using simple thresholding and segmentation process. The mosaic products results from both method shown good results visually but the detailed histogram shown that the S-1 Remove GRD Border Noise results still have a very low value pixels in the black-fill area while the Random Noise Removal removed all of the noise. PSNR of raw data mosaic, GRD Border Noise and Random Noise Removal results sequentially 8.5, 18.6 and 19.7 dB indicated that Random Noise Removal get the highest similarity to reference data.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8079 DO - 10.18517/ijaseit.9.1.8079