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TanDEM-X multiparametric data features in sea ice classification over the Baltic sea

Geo-Spatial Information Science 2021年第2期24卷 313-332,I0009页

作者： Marjan Marbouti oleg antropov Jaan Praks Patrick B.Eriksson Vahid Arabzadeh Eero Rinne Matti Leppärant Institute for Atmospheric and Earth System Research/Physics Faculty of ScienceUniversity of HelsinkiHelsinkiFinland VTT Technical Research Centre of Finland EspooFinland Aalto University Department of Electronics and NanoengineeringEspooFinland Finnish Meteorological Institute HelsinkiFinland Aalto University Department of Applied PhysicsNew Energy Technologies GroupEspooFinland

In this study,we assess the potential of X-band Interferometric Synthetic Aperture Radar imagery for automated classification of sea ice over the Baltic Sea.A bistatic SAR scene acquired by the TanDEM-X mission over the Bothnian Bay in March of 2012 was used in the analysis.Backscatter intensity,interferometric coherence magnitude,and interferometric phase have been used as informative features in several classification experiments.Various combinations of classification features were evaluated using Maximum likelihood(ML),Random Forests(RF)and Support Vector Machine(SVM)classifiers to achieve the best possible discrimination between open water and several sea ice types(undeformed ice,ridged ice,moderately deformed ice,brash ice,thick level ice,and new ice).Adding interferometric phase and coherence-magnitude to backscatter-intensity resulted in improved overall classification per-formance compared to using only backscatter-intensity.The RF algorithm appeared to be slightly superior to SVM and ML due to higher overall accuracies,however,at the expense of somewhat longer processing time.The best overall accuracy(OA)for three methodologies were achieved using combination of all tested features were 71.56,72.93,and 72.91%for ML,RF and SVM classifiers,respectively.Compared to OAs of 62.28,66.51,and 63.05%using only backscatter intensity,this indicates strong benefit of SAR interferometry in discriminating different types of sea ice.In contrast to several earlier studies,we were particularly able to successfully discriminate open water and new ice classes.

关键词： Sea ice classification Random Forests(RF) Maximum Likelihood(ML) Support Vector Machine(SVM) backscatter coherence sar interferometry Synthetic Aperture Radar(SAR)

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