Denoising Sentinel-1 Extra-Wide Mode Cross-Polarization Images over Sea Ice

Sun, Yan; Li, Xiao-Ming

doi:10.20944/preprints202006.0092.v1

Cited by 9 publications

(14 citation statements)

References 20 publications

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“…∑ ∑ , ( 1 0 ) Fig. 2 shows the six texture images derived from a denoised S1 EW HV-polarized image using the method proposed in [31]. As expected, these texture features show distinguishable differences between sea ice and open water that sea ice usually presents more randomness and complexity variations in the SAR images.…”

Section: Methodsmentioning

confidence: 71%

“…In [31], we solved the problem of denoising the S1 EW data in HV polarization. This is the basis of the proposed algorithm in this paper.…”

Section: Discussionmentioning

confidence: 99%

“…In this section, a detailed description of the proposed method for deriving sea ice cover from S1 EW HV-polarized data is presented. First, the EW data are preprocessed using the denoising method proposed by us in [31]. Then, the process of ice cover extraction is described in four major steps: 1) calculation of texture features using GLCM, 2) automatic extraction and classification of training samples, 3) training of the SVM and 4) application of the trained SVM on the S1 EW image.…”

Section: Methodsmentioning

confidence: 99%

“…For the algorithm development and validation, 728 scenes of S1A and S1B EW data in HV polarization acquired in the Arctic during the melting season from July 1 to September 30, 2018, were used. Prior to using these data to derive sea ice cover information, all these HV-polarized EW data are denoised using the method proposed in [31].…”

Section: A Sentinel-1 Extra-wide Swath Datamentioning

confidence: 99%

See 3 more Smart Citations

Extraction of Sea Ice Cover by Sentinel-1 SAR Based on Support Vector Machine With Unsupervised Generation of Training Data

Sun

Zhang

2021

IEEE Trans. Geosci. Remote Sensing

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In this paper, we focus on developing a novel method to extract sea ice cover (i.e., discrimination/classification of sea ice and open water) using Sentinel-1 (S1) cross-polarization (vertical-horizontal, VH or horizontal-vertical, HV) data in extra wide (EW) swath mode based on the machine learning algorithm support vector machine (SVM). The classification basis includes the S1 radar backscatter coefficients and texture features that are calculated from S1 data using the gray level cooccurrence matrix (GLCM). Different from previous methods where appropriate samples are manually selected to train the SVM to classify sea ice and open water, we proposed a method of unsupervised generation of the training samples based on two GLCM texture features, i.e. entropy and homogeneity, that have contrasting characteristics on sea ice and open water. We eliminate the most uncertainty of selecting training samples in machine learning and achieve automatic classification of sea ice and open water by using S1 EW data. The comparison shows good agreement between the SAR-derived sea ice cover using the proposed method and a visual inspection, of which the accuracy reaches approximately 90%-95% based on a few cases. Besides this, compared with the analyzed sea ice cover data Ice Mapping System (IMS) based on 728 S1 EW images, the accuracy of extracted sea ice cover by using S1 data is more than 80%.

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Section: Methodsmentioning

confidence: 71%

“…In [31], we solved the problem of denoising the S1 EW data in HV polarization. This is the basis of the proposed algorithm in this paper.…”

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: A Sentinel-1 Extra-wide Swath Datamentioning

confidence: 99%

See 2 more Smart Citations

Extraction of Sea Ice Cover by Sentinel-1 SAR Based on Support Vector Machine With Unsupervised Generation of Training Data

Sun

Zhang

2021

IEEE Trans. Geosci. Remote Sensing

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“…Accurate radar calibration and specialized denoising schemes should be developed for every radar sensor mode to remove all these noises in cross-polarized SAR images. Sun and Li proposed a method to improve the quality of Sentinel-1 extra-wide (S1-EW) mode cross-polarized image for sea ice monitoring [45]. Similar algorithms should also be used in the future to eliminate the noise of Radarsat-2 ScanSAR images before JSTARS-2020-00786 using them to derive hurricane winds.…”

Section: Discussionmentioning

confidence: 99%

An Improved Asymmetric Hurricane Parametric Model Based on Cross-Polarization SAR Observations

Wang

Yang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Retrieval of Ocean Wave Heights From Spaceborne SAR in the Arctic Ocean With a Neural Network

Huang

2021

JGR Oceans

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Prior to the launch of the Chinese French Oceanic Satellite (CFOSAT) with its onboard Surface Waves Investigation and Monitoring (SWIM) sensor, the only sensor capable of imaging ocean waves in two dimensions from space was the spaceborne synthetic aperture radar (SAR), which provides images with high spatial resolution. The SAR imaging mechanism of ocean waves is complex which is generally explained by three modulations: tilt modulation, hydrodynamic modulation and velocity bunching (Alpers et al., 1981; Valenzuela, 1978). While tilt and hydrodynamic modulations are also shared by real-aperture radar as the dominant imaging mechanisms of ocean waves, velocity bunching is unique for SAR to image ocean waves. The moving scatterer of water particles with a velocity either toward or away from a moving SAR sensor, causes an azimuthal shift in SAR images. In addition, velocity bunching in the SAR resolution cell leads to an azimuth cutoff , that is, the minimum SAR-detectable wavelength of ocean waves traveling in the azimuth direction. Therefore, the nonlinearity of SAR ocean wave imaging complicates their retrieval. In the following, we briefly summarize the existing methods used to retrieve ocean wave information in terms of both two-dimensional spectra and integral wave parameters. The Max Planck Institute (MPI) scheme developed by Hasselmann and Hasselmann (1991) and Hasselmann et al. (1996) is a widely used method to retrieve two-dimensional ocean wave spectra from spaceborne

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Denoising Sentinel-1 Extra-Wide Mode Cross-Polarization Images over Sea Ice

Cited by 9 publications

References 20 publications

Extraction of Sea Ice Cover by Sentinel-1 SAR Based on Support Vector Machine With Unsupervised Generation of Training Data

Extraction of Sea Ice Cover by Sentinel-1 SAR Based on Support Vector Machine With Unsupervised Generation of Training Data

An Improved Asymmetric Hurricane Parametric Model Based on Cross-Polarization SAR Observations

Retrieval of Ocean Wave Heights From Spaceborne SAR in the Arctic Ocean With a Neural Network

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