Noise removal using thresholding and segmentation for random noise Sentinel-1 data

Dyatmika, Haris Suka; Sambodo, Katmoko Ari; Budiono, Marendra Eko; Hendayani,

doi:10.1088/1755-1315/54/1/012105

Cited by 4 publications

(5 citation statements)

References 2 publications

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“…The selected method to the geometric correction was range Doppler terrain correction using SNAP. After all processes, the noise were strongly influenced the mosaic product as shown in Figure 2c [12], [13], [6]. The black-fill area have a very low value compare to the normal backscatter value.…”

Section: A Data and Problem Settingmentioning

confidence: 99%

“…Some research have been done to fix the problem. The noise can be removed significantly using thresholding and segmentation [6].…”

Section: Introductionmentioning

confidence: 99%

“…Limit the noise suspected pixels area around the border (left and right side) of the scene • Step 2: Perform a simple thresholding process to the backscatter amplitude of the Sentinel-1 data • Step 3: Perform a segmentation to noise suspected area Perform a simple thresholding process with the segmented area size • Step 5: Mask the noise • Step 6: Remove the masked noise[6].…”

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confidence: 99%

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

Dyatmika¹,

Sambodo²,

Arief³

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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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.

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Section: A Data and Problem Settingmentioning

confidence: 99%

“…Some research have been done to fix the problem. The noise can be removed significantly using thresholding and segmentation [6].…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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

Dyatmika¹,

Sambodo²,

Arief³

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…In the literature, only three papers have been published that have tried to address the problem. In [ 12 ], Dyatmika et al presented a method that first thresholds the VH and VV bands of a IW GRD product by applying two fixed thresholds and then segments the noise to remove big areas. The method was tested on two images and assessed by visual interpretation.…”

Section: Introductionmentioning

confidence: 99%

An Operational Tool for the Automatic Detection and Removal of Border Noise in Sentinel-1 GRD Products

Stasolla

Neyt

2018

Sensors

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The presence of border noise in Sentinel-1 Ground Range Detected (GRD) products is an undesired processing artifact that limits their full exploitation in a number of applications. All of the Sentinel-1 GRD products generated before March 2018—more than 800,000—are affected by this particular type of noise. In March 2018, an official fix was deployed that solved the problem for a large portion of the newly generated products, but it did not cover the entire range of products, hence the need for an operational tool that is able to effectively and consistently remove border noise in an automated way. Currently, a few solutions have been proposed that try to address the problem, but all of them have limitations. The scope of this paper is therefore to present a new method based on mathematical morphology for the automatic detection and masking of border noise in Sentinel-1 GRD products that is able to overcome the existing limitations. To evaluate the performance of the method, a detailed numerical assessment was carried out, using, as a benchmark, the ‘Remove GRD Border Noise’ module integrated in ESA’s Sentinel Application Platform. The results showed that the proposed method is capable of very accurately removing the undesired noisy pixels from GRD images, regardless of their acquisition mode, polarization, or resolution and can cope with challenging features within the image scenes that typically affect other approaches.

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“…When the backscattering signals are low, e.g., from water, applying this tool can either leave lots of residuals or take off many valid data. A few other methods have been recently proposed to deal with these noise, and better results are shown comparing to those from the SNAP tool [6,7]. In order to appropriately remove border noises in scenes specifically over sea ice and open water, a method using line-by-line scanning and filtering is proposed in this paper.…”

Section: Introductionmentioning

confidence: 99%

Sentinel-1 Data Border Noise Removal and Seamless Synthetic Aperture Radar Mosaic Generation

Luo

Flett

2018

The 2nd International Electronic Conference on Remote Sensing

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Abstract:The Canadian Ice Service (CIS, Ottawa, ON, Canada) is receiving hundreds of Synthetic Aperture Radar (SAR) images daily with almost a complete coverage of Canada navigable waters for the monitoring and mapping of seasonal sea and lake ice. In order to efficiently use and analyze such a large amount and wide areal extent of data, short-term (i.e., 12 h to a few days) highresolution mosaic products are of interest. Among these SAR images, Sentinel-1 data have been known to have an issue of border noise which needs to be removed before generating a seamless mosaic. A method using line-by-line scanning and filtering is proposed, which traces an extreme jump between two neighboring pixels along every scan line. The results show that this method can remove the noise precisely while retaining the rest of the valid data. For visual display, analysis, and interpretation, as done at the CIS, a tone-balanced smooth mosaic is of interest and value to ice analysts in displaying the overall ice distribution and in viewing and comparing cross-region ice conditions. To address this, a scene boundary match balancing method is developed. These shortterm mosaic products are proved very helpful in daily ice analysis and macroscopic ice drift measurement.

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Noise removal using thresholding and segmentation for random noise Sentinel-1 data

Cited by 4 publications

References 2 publications

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

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

An Operational Tool for the Automatic Detection and Removal of Border Noise in Sentinel-1 GRD Products

Sentinel-1 Data Border Noise Removal and Seamless Synthetic Aperture Radar Mosaic Generation

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