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

Abstract

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.

Research on Appearance Detection, Sorting, and Regrouping Technology of Retired Batteries for Electric Vehicles

“…GMM is a probabilistic-based clustering method [34], which is computed by iterative optimization using the expectation maximization (EM) algorithm [35]. The EM algorithm is an iterative approach to solving a special maximum likelihood problem.…”

Sea Ice Extraction in SAR Images via a Spatially Constrained Gamma Mixture Model

“…Due to the simple structure and easy implementation of Gaussian distributions, the Gaussian mixture model (GMM) is commonly used for statistical modeling in image segmentation [17]. For example, Bao et al [18] proposed a water extraction algorithm in SAR images, and GMM was used for the statistical modeling of targets and backgrounds based on the initial extraction of homogeneous regions using the Otsu algorithm; Liu et al [19] used Generalized GMM to achieve change detection based on the segmentation of different images; Barzycka et al [20] tested and validated GMM-based methods for extracting glacier zones, and the result indicated that the GMM-EM (Expectation maximization) method was suitable for extracting glacier zones based on sigma0 and Pauli decomposition of SAR images. Moreover, GMM can also be applied to domain adaptation research in machine learning [21].…”