Polarimetric decomposition of microwave-band freshwater ice SAR data: Review, analysis, and future directions

Ferguson, Jake E.; Gunn, Grant E.

doi:10.1016/j.rse.2022.113176

Cited by 8 publications

(4 citation statements)

References 86 publications

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“…SAR imagery has been also used for lake ice classification (open water and various ice types, ice break-up) and to detect floating and bedfast lake ice regimes in the Arctic, e.g., [61][62][63][64]. In addition, using polarimetric decomposition in order to understand scattering processes in freshwater ice has been investigated recently [65].…”

Section: Previous Studiesmentioning

confidence: 99%

X- and Ku-Band SAR Backscattering Signatures of Snow-Covered Lake Ice and Sea Ice

Veijola,

Cohen,

Mäkynen

et al. 2024

Remote Sensing

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In this work, backscattering signatures of snow-covered lake ice and sea ice from X- and Ku-band synthetic aperture radar (SAR) data are investigated. The SAR data were acquired with the ESA airborne SnowSAR sensor in winter 2012 over Lake Orajärvi in northern Finland and over landfast ice in the Bay of Bothnia of the Baltic Sea. Co-incident with the SnowSAR acquisitions, in situ snow and ice data were measured. In addition, time series of TerraSAR-X images and ice mass balance buoy data were acquired for Lake Orajärvi in 2011–2012. The main objective of our study was to investigate relationships between SAR backscattering signatures and snow depth over lake and sea ice, with the ultimate objective of assessing the feasibility of retrieval of snow characteristics using X- and Ku-band dual-polarization (VV and VH) SAR over freshwater or sea ice. This study constitutes the first comprehensive survey of snow backscattering signatures at these two combined frequencies over both lake and sea ice. For lake ice, we show that X-band VH-polarized backscattering coefficient (σo) and the Ku-band VV/VH-ratio exhibited the highest sensitivity to the snow depth. For sea ice, the highest sensitivity to the snow depth was found from the Ku-band VV-polarized σo and the Ku-band VV/VH-ratio. However, the observed relations were relatively weak, indicating that at least for the prevailing snow conditions, obtaining reliable estimates of snow depth over lake and sea ice would be challenging using only X- and Ku-band backscattering information.

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Section: Previous Studiesmentioning

confidence: 99%

X- and Ku-Band SAR Backscattering Signatures of Snow-Covered Lake Ice and Sea Ice

Veijola,

Cohen,

Mäkynen

et al. 2024

Remote Sensing

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“…The polarization diversity makes PolSAR an advantageous tool for terrain surface classification. Based on polarization, many frequently used classification methods are based on polarimetric decomposition 1 , 14 – 16 Polarimetric analysis starts by encoding the received radar signal in a scattering matrix S, given as S=[ShhShυSυhSυυ].…”

Section: Polsar Data Analysis and Feature Extractionmentioning

confidence: 99%

“…Based on polarization, many frequently used classification methods are based on polarimetric decomposition. 1,[14][15][16] Polarimetric analysis starts by encoding the received radar signal in a scattering matrix S, given as E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 0 1 ; 1 1 7 ; 6 7 1…”

Section: Polsar Data Analysis and Feature Extractionmentioning

confidence: 99%

Land cover analysis of PolSAR images using probabilistic voting ensemble and integrated support vector machine

AboElenean,

Helmy,

ElTohamy

et al. 2023

J. Appl. Rem. Sens.

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.Land cover classification is a vital application of polarimetric synthetic aperture radar (PolSAR) images in various fields, such as agriculture monitoring and urban assessment. We introduce a modified and enhanced PolSAR image classification method, combining six decomposition techniques, a support vector machine (SVM) based classifier, and a probabilistic voting ensemble (PVE) model. Our method addresses the challenges posed by the complexity of PolSAR data and the limited availability of labeled samples. The core of our approach lies in integrating multiple decomposition techniques as feature extractors, aiming to capture diverse scattering behaviors and uncover valuable information related to land cover characteristics. These techniques include the Huynen, Cloude, Freeman and Durden, HAAlpha, Yamaguchi, and Vanzyl decomposition methods. The extracted features are then utilized as inputs for training the SVM base classifier. To enhance classification performance, a PVE model is used to combine predictions from each decomposition technique, considering the individual prediction confidence and the characteristics of the decomposition methods. The decision fusion process is applied to integrate diverse predictions based on the majority voting and estimated class probability, providing a more robust and reliable final label prediction and thereby improving the overall accuracy of the classification process. Experimental analyses are conducted on airborne and spaceborne PolSAR images, covering various bands and land cover types, to evaluate the effectiveness and robustness of our proposed method. The experimental results demonstrate that our approach yields more confident class predictions than alternative methods.

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“…[47][48][49][50]. In addition, using polarimetric decomposition in order to understand scattering processes in freshwater ice has been investigated recently [51].…”

Section: Figurementioning

confidence: 99%

X- and Ku-Band Sar Backscattering Signatures of Snow-Covered Lake Ice and Sea Ice

Veijola,

Cohen,

Mäkynen

et al. 2023

Preprint

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In this work, backscattering signatures of snow-covered lake ice and sea ice from X- and Ku-band synthetic aperture radar (SAR) data are investigated. The SAR data were acquired with the ESA airborne SnowSAR sensor in winter 2012 over Lake Orajärvi in northern Finland and over landfast ice in the Bay of Bothnia of the Baltic Sea. Co-incident with the SnowSAR acquisitions in-situ snow and ice data were measured. In addition, time series of TerraSAR-X images and ice mass balance buoy data were acquired for the Lake Orajärvi in 2011-12. The main objective of our study was to investigate relationships between SAR backscattering signatures and snow depth over lake and sea ice, with the ultimate objective of assessing the feasibility of retrieval of snow characteristics using X- and Ku-band dual-polarisation (VV and VH) SAR over freshwater or sea ice. This study constitutes the first comprehensive survey of snow backscattering signatures at these two combined frequencies over both lake and sea ice. For lake ice, we show that X-band VH-polarized backscattering coefficient (σo) and the Ku-band VV/VH-ratio exhibited the highest sensitivity to the snow depth. For sea ice, the highest sensitivity to the snow depth was found from the Ku-band VV-polarised σo and the Ku-band VV/VH-ratio. However, the observed relations were relatively weak, indicating that at least for the prevailing snow conditions, obtaining reliable estimates of snow depth over lake and sea ice would be challenging using only X- and Ku-band backscattering information.

show abstract

Polarimetric decomposition of microwave-band freshwater ice SAR data: Review, analysis, and future directions

Cited by 8 publications

References 86 publications

X- and Ku-Band SAR Backscattering Signatures of Snow-Covered Lake Ice and Sea Ice

X- and Ku-Band SAR Backscattering Signatures of Snow-Covered Lake Ice and Sea Ice

Land cover analysis of PolSAR images using probabilistic voting ensemble and integrated support vector machine

X- and Ku-Band Sar Backscattering Signatures of Snow-Covered Lake Ice and Sea Ice

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