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

Abstract

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.

Seamless Mosaic Using Removal Sunglint to Support a Large-Scale Mapping Acceleration

“…Figure 4 show the appearance of the cloudwiz tool used in the process of removing sunglints. Seamless mosaics can be produced by making cutlines and color balancing, so that in the process of selecting the right data it can produce seamless mosaics (Chen et al, 2014;Cheng et al, 2017;Cudahy et al, 2010;Luo & Flett, 2018). But in fact, not all data are ideal as expected, sometimes some data are less than ideal, such as in this study, where one of the data is sunglint.…”

RFI Artefacts Detection in Sentinel-1 Level-1 SLC Data Based On Image Processing Techniques

“…Another type of InSAR data failure described in the literature is a border noise [24][25][26][27]. This undesired processing artefact appeared in all of the Sentinel-1 GRD products generated before March 2018 [24].…”

“…This undesired processing artefact appeared in all of the Sentinel-1 GRD products generated before March 2018 [24]. Although this problem has been solved for the newly generated products, it did not cover the entire range of products and researchers still develop new methods and tools to effectively detect and remove this particular type of noise [24][25][26][27].…”

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

“…This issue has clearly hindered the full exploitation of Sentinel-1 data and has impacted many applications, such as ship detection [ 6 ], wind speed retrieval [ 7 ], and sea ice detection [ 8 ]—where fluctuations in the background distribution caused by imhomogeneities (e.g., sea-clutter, beam seams) typically raise the number of false alarms—, or the automatic generation of stacks of images and mosaics—where data gaps should be avoided [ 9 ]. For these reasons, version 2.90 of the IPF was deployed on 13 March 2018 to address the problem [ 4 ] and since then, the newly generated IW and EW products have all of the no-value pixels at the image borders correctly set to zero.…”

“…It only focused on near range and far range border noise, with no mention of the border noise in the azimuth direction. In [ 9 ], a border noise removal tool was implemented within the automated mosaicking system of the Canadian Ice Service (CIS). The method is based on a four-round scanning that identifies the data/noise border based on the adjacent pixels ratio.…”