Topographic performance evaluation of the RADARSAT-2/3 tandem mission

Caves, R.; Luscombe, A.P.; Lee, P.F.; James, K.

doi:10.1109/igarss.2002.1025743

Cited by 5 publications

(3 citation statements)

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“…As a pre-processing step for sigma0 retrieval (Figure 2), the SAR data was calibrated [59,60] and filtered using a Refined Lee filter with a 11 × 11 window size, a 3 × 3 target window size, and a single number of looks [18]. A terrain correction algorithm was then applied [59,61] using ArcticDEM-based Digital Elevation Models [33,62]. In order to mimic dual-pol SAR satellite data, only sigma0 values for HH and HV polarization were chosen and scaled to decibels.…”

Section: Gmm-em Classification Of Dual-pol Sar Sigma0mentioning

confidence: 99%

“…A T3 polarimetric matrix was then generated [18] and filtered using the Refined Lee filter [18], as described in Section 3.2.2. Based on this, Pauli decomposition was performed [18] and the data were geometrically corrected [59,61] using the DEMs described in Section 3.2.2. Results were scaled to decibels.…”

Section: Gmm-em Classification Of Quad-pol Sar Pauli Decompositionmentioning

confidence: 99%

“…Due to software limitations, the H/α Wishart segmentation was applied to a full SAR imagery, not an AOI subset (Figure 1). As a final step, a terrain correction was applied [59,61]. The described process was performed in PolSARPro 6.0 software [76], and the workflow is based on [77].…”

Section: H/α Wishart Segmentation Of Quad-pol Sar Datamentioning

confidence: 99%

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Comparison of Three Methods for Distinguishing Glacier Zones Using Satellite SAR Data

et al. 2023

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Changes in glacier zones (e.g., firn, superimposed ice, ice) are good indicators of glacier response to climate change. There are few studies of glacier zone detection by SAR that are focused on more than one ice body and validated by terrestrial data. This study is unique in terms of the dataset collected—four C- and L-band quad-pol satellite SAR images, Ground Penetrating Radar data, shallow glacier cores—and the number of land ice bodies analyzed, namely, three tidewater glaciers in Svalbard and one ice cap in Iceland. The main aim is to assess how well popular methods of SAR analysis perform in distinguishing glacier zones, regardless of factors such as the morphologic differences of the ice bodies, or differences in SAR data. We test and validate three methods of glacier zone detection: (1) Gaussian Mixture Model–Expectation Maximization (GMM-EM) clustering of dual-pol backscattering coefficient (sigma0); (2) GMM-EM of quad-pol Pauli decomposition; and (3) quad-pol H/α Wishart segmentation. The main findings are that the unsupervised classification of both sigma0 and Pauli decomposition are promising methods for distinguishing glacier zones. The former performs better at detecting the firn zone on SAR images, and the latter in the superimposed ice zone. Additionally, C-band SAR data perform better than L-band at detecting firn, but the latter can potentially separate crevasses via the classification of sigma0 or Pauli decomposition. H/α Wishart segmentation resulted in inconsistent results across the tested cases and did not detect crevasses on L-band SAR data.

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