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

作　　者：Marjan Marbouti Oleg Antropov Jaan Praks Patrick B.Eriksson Vahid Arabzadeh Eero Rinne Matti Leppärant 

机构地区：[1]Institute for Atmospheric and Earth System Research/Physics,Faculty of Science,University of Helsinki,Helsinki,Finland [2]VTT Technical Research Centre of Finland,Espoo,Finland [3]Aalto University,Department of Electronics and Nanoengineering,Espoo,Finland [4]Finnish Meteorological Institute,Helsinki,Finland [5]Aalto University,Department of Applied Physics,New Energy Technologies Group,Espoo,Finland

出　　处：《Geo-Spatial Information Science》2021年第2期313-332,I0009,共21页地球空间信息科学学报（英文）

基　　金：This research was supported by Academy of Finland under Grant no.296628.

摘　　要：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) 

分 类 号：P731.15[天文地球—海洋科学]