Classification of sea-ice images using a dual-polarized radar

The Canadian RADARSAT Constellation Mission (RCM) has passed its early operation phase with the performance evaluation being currently active. This evaluation aims to confirm that the innovative design of the mission’s synthetic aperture radar (SAR) meets the expectations of intended users. In this study, we provide an overview of initial results obtained for three high-priority applications; flood mapping, sea ice analysis, and wetland classification. In our study, the focus is on results obtained using not only linear polarization, but also the adopted Compact Polarimetric (CP) architecture in RCM. Our study shows a promising level of agreement between RCM and RADARSAT-2 performance in flood mapping using dual-polarized HH-HV SAR data over Red River, Manitoba, suggesting smooth continuity between the two satellite missions for operational flood mapping. Visual analysis of coincident RCM CP and RADARSAT-2 dual-polarized HH-HV SAR imagery over the Resolute Passage, Canadian Central Arctic, highlighted an improved contrast between sea ice classes in dry ice winter conditions. A statistical analysis using selected sea ice samples confirmed the increased contrast between thin and both rough and deformed ice in CP SAR. This finding is expected to enhance Canadian Ice Service’s (CIS) operational visual analysis of sea ice in RCM SAR imagery for ice chart production. Object-oriented classification of a wetland area in Newfoundland and Labrador by fusion of RCM dual-polarized VV-VH data and Sentinel-2 optical imagery revealed promising classification results, with an overall accuracy of 91.1% and a kappa coefficient of 0.87. Marsh presented the highest user’s and producer’s accuracies (87.77% and 82.08%, respectively) compared to fog, fen, and swamp.

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Sea Ice and Open Water Classification of SAR Images Using a Deep Learning Model

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss39084.2020.9323990 ◽

2020 ◽

Author(s):

Yibin Ren ◽

Huan Xu ◽

Bin Liu ◽

Xiaofeng Li

Keyword(s):

Deep Learning ◽

Sea Ice ◽

Open Water ◽

Learning Model ◽

Sar Images ◽

Water Classification ◽

Deep Learning Model

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Classification of sea ice types from ScanSAR RADARSAT images using pulse-coupled neural networks

IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174) ◽

10.1109/igarss.1998.702260 ◽

1998 ◽

Cited By ~ 1

Author(s):

J. Karvonen ◽

M. Simila

Keyword(s):

Neural Networks ◽

Sea Ice ◽

Coupled Neural Networks ◽

Pulse Coupled Neural Networks

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Classification of Low-Salinity Sea Ice Types by Dual-Frequency Multipolarization Scatterometer

[Proceedings] IGARSS'91 Remote Sensing: Global Monitoring for Earth Management ◽

10.1109/igarss.1991.579289 ◽

2005 ◽

Cited By ~ 3

Author(s):

M. Hallikainen ◽

T. Tares ◽

M. Toikka ◽

K. Heiska

Keyword(s):

Sea Ice ◽

Dual Frequency ◽

Low Salinity

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Operational algorithm for ice/water classification on dual-polarized RADARSAT-2 images

10.5194/tc-2016-131 ◽

2016 ◽

Cited By ~ 3

Author(s):

Natalia Zakhvatkina ◽

Anton Korosov ◽

Stefan Muckenhuber ◽

Stein Sandven ◽

Mohamed Babiker

Keyword(s):

Sea Ice ◽

Texture Features ◽

Open Water ◽

Support Vector ◽

Technical Problems ◽

Additional Information ◽

Franz Josef Land ◽

Water Classification ◽

Dual Polarized ◽

Supervised Image Classification

Abstract. Synthetic aperture radar (SAR) data from RADARSAT-2 (RS2) taken in dual-polarization mode provide additional information for discriminating sea ice and open water compared to single-polarization data. We have developed a fully automatic algorithm to distinguish between open water (rough/calm) and sea ice based on dual-polarized RS2 SAR images. Several technical problems inherent in RS2 data were solved on the pre-processing stage including thermal noise reduction in HV-polarization channel and correction of angular backscatter dependency on HH-polarization. Texture features are used as additional information for supervised image classification based on Support Vector Machines (SVM) approach. The main regions of interest are the ice-covered seas between Greenland and Franz Josef Land. The algorithm has been trained using 24 RS2 scenes acquired during winter months in 2011 and 2012, and validated against the manually derived ice chart product from the Norwegian Meteorological Institute. Between 2013 and 2015, 2705 RS2 scenes have been utilised for validation and the average classification accuracy has been found to be 91 &pm; 4 %.

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A Comparison of Machine Learning Algorithms for the Segmentation and Classification of Snow Micro Penetrometer Profiles on Arctic Sea Ice

10.5194/egusphere-egu21-15637 ◽

2021 ◽

Author(s):

Julia Kaltenborn ◽

Viviane Clay ◽

Amy R. Macfarlane ◽

Joshua Michael Lloyd King ◽

Martin Schneebeli

Keyword(s):

Machine Learning ◽

Sea Ice ◽

Arctic Sea Ice ◽

Machine Learning Algorithms ◽

Training Data ◽

Support Vector ◽

Snow Layer ◽

Arctic Sea ◽

Execution Speed

Snow-layer classification is an essential diagnostic task for a wide variety of cryospheric science and climate research applications. Traditionally, these measurements are made in snow pits, requiring trained operators and a substantial time commitment. The SnowMicroPen (SMP), a portable high-resolution snow penetrometer, has been demonstrated as a capable tool for rapid snow grain classification and layer type segmentation through statistical inversion of its mechanical signal. The manual classification of the SMP profiles requires time and training and becomes infeasible for large datasets.Here, we introduce a novel set of SMP measurements collected during the MOSAiC expedition and apply Machine Learning (ML) algorithms to automatically classify and segment SMP profiles of snow on Arctic sea ice. To this end, different supervised and unsupervised ML methods, including Random Forests, Support Vector Machines, Artificial Neural Networks, and k-means Clustering, are compared. A subsequent segmentation of the classified data results in distinct layers and snow grain markers for the SMP profiles. The models are trained with the dataset by King et al. (2020) and the MOSAiC SMP dataset. The MOSAiC dataset is a unique and extensive dataset characterizing seasonal and spatial variation of snow on the central Arctic sea-ice.We will test and compare the different algorithms and evaluate the algorithms&#8217; effectiveness based on the need for initial dataset labeling, execution speed, and ease of implementation. In particular, we will compare supervised to unsupervised methods, which are distinguished by their need for labeled training data.The implementation of different ML algorithms for SMP profile classification could provide a fast and automatic grain type classification and snow layer segmentation. Based on the gained knowledge from the algorithms&#8217; comparison, a tool can be built to provide scientists from different fields with an immediate SMP profile classification and segmentation.&#160;&#160;King, J., Howell, S., Brady, M., Toose, P., Derksen, C., Haas, C., & Beckers, J. (2020). Local-scale variability of snow density on Arctic sea ice. The Cryosphere, 14(12), 4323-4339, https://doi.org/10.5194/tc-14-4323-2020.

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