Cross-platform application of a sea ice classification method considering incident angle dependency of backscatter intensity and its use in separating level and deformed ice

Abstract. Wide-swath C-band synthetic aperture radar (SAR) has been used for sea ice classification and estimates of sea ice drift and deformation since it first became widely available in the 1990s. Here, we examine the potential to distinguish surface features created by sea ice deformation using ice type classification of SAR data. To perform this task with extended spatial and temporal coverage, we investigate the cross-platform transferability between training sets derived from Sentinel-1 Extra Wide (S1 EW) and RADARSAT-2 (RS2) ScanSAR Wide A (SCWA) and Fine Quad-polarimetric (FQ) data, as the same radiometrically calibrated backscatter coefficients are expected from these two C-band SAR platforms. For this, we use a novel sea ice classification method developed based on Arctic-wide S1 EW training, which considers the ice-type-dependent change of SAR backscatter intensity with incident angle (IA). This study focuses on the region near Fram Strait north of Svalbard to utilize expert knowledge of ice conditions from co-authors who participated in the Norwegian young sea ICE (N-ICE2015) expedition in the region. Separate training sets for S1 EW, RS2 SCWA and RS2 FQ data are derived using manually drawn polygons of different ice types, and are used to re-train the classifier. Results show that although the best classification accuracy is achieved for each dataset using its own training, different training sets yield similar results and IA slopes, with the exception of leads with calm open water, nilas or newly formed ice (the “leads”' class). This is found to be caused by different noise floor configurations of S1 and RS2 data, which lead to different IA slopes of this class. This indicates that dataset-specific re-training is needed for leads in the cross-platform application of the classifier. Based on the classifier thus re-trained for each dataset, the classification scheme is altered to target the separation of level and deformed ice, which enables direct comparison with independently derived sea ice deformation maps. The comparisons show that the classification of C-band SAR can be used to distinguish areas of ice divergence occupied by leads, young ice and level first-year ice (LFYI). However, it has limited capacity in delineating areas of ice deformation due to ambiguities in ice types represented by classes with higher backscatter intensities. This study provides reference to future studies seeking cross-platform application of training sets so they are fully utilized, and we expect further development of the classifier and the inclusion of other SAR datasets to enable image classification-based ice deformation detection using only satellite SAR data.

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Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification

Remote Sensing ◽

10.3390/rs13040552 ◽

2021 ◽

Vol 13 (4) ◽

pp. 552

Author(s):

Johannes Lohse ◽

Anthony P. Doulgeris ◽

Wolfgang Dierking

Keyword(s):

Sea Ice ◽

Incident Angle ◽

Texture Features ◽

Data Set ◽

Backscatter Intensity ◽

Medium Resolution ◽

Suitable Combination ◽

Reliable Classification ◽

Occurrence Matrix

Robust and reliable classification of sea ice types in synthetic aperture radar (SAR) images is needed for various operational and environmental applications. Previous studies have investigated the class-dependent decrease in SAR backscatter intensity with incident angle (IA); others have shown the potential of textural information to improve automated image classification. In this work, we investigate the inclusion of Sentinel-1 (S1) texture features into a Bayesian classifier that accounts for linear per-class variation of its features with IA. We use the S1 extra-wide swath (EW) product in ground-range detected format at medium resolution (GRDM), and we compute seven grey level co-occurrence matrix (GLCM) texture features from the HH and the HV backscatter intensity in the linear and logarithmic domain. While GLCM texture features obtained in the linear domain vary significantly with IA, the features computed from the logarithmic intensity do not depend on IA or reveal only a weak, approximately linear dependency. They can therefore be directly included in the IA-sensitive classifier that assumes a linear variation. The different number of looks in the first sub-swath (EW1) of the product causes a distinct offset in texture at the sub-swath boundary between EW1 and the second sub-swath (EW2). This offset must be considered when using texture in classification; we demonstrate a manual correction for the example of GLCM contrast. Based on the Jeffries–Matusita distance between class histograms, we perform a separability analysis for 57 different GLCM parameter settings. We select a suitable combination of features for the ice classes in our data set and classify several test images using a combination of intensity and texture features. We compare the results to a classifier using only intensity. Particular improvements are achieved for the generalized separation of ice and water, as well as the classification of young ice and multi-year ice.

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Contextual Classification of Sea-Ice Types Using Compact Polarimetric SAR Data

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2019.2913796 ◽

2019 ◽

Vol 57 (10) ◽

pp. 7476-7491 ◽

Cited By ~ 1

Author(s):

Mohsen Ghanbari ◽

David A. Clausi ◽

Linlin Xu ◽

Mingzhe Jiang

Keyword(s):

Sea Ice ◽

Polarimetric Sar ◽

Sar Data

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Classification of fully polarimetric single- and dual-frequency SAR data of sea ice using the Wishart statistics

Canadian Journal of Remote Sensing ◽

10.5589/m04-060 ◽

2005 ◽

Vol 31 (1) ◽

pp. 61-72 ◽

Cited By ~ 18

Author(s):

Bernd Scheuchl ◽

Ian Cumming ◽

Irena Hajnsek

Keyword(s):

Sea Ice ◽

Dual Frequency ◽

Sar Data

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Convolutional Neural Networks for Classification of Sea Ice Types in Sentinel-1 SAR Data

10.5194/egusphere-egu21-10580 ◽

2021 ◽

Author(s):

Anton Korosov ◽

Hugo Boulze ◽

Julien Brajard

Keyword(s):

Random Forest ◽

Sea Ice ◽

Classification Accuracy ◽

Thermal Noise ◽

Noise Removal ◽

The Arctic ◽

First Year ◽

Sar Data ◽

The Impact

A new algorithm for classification of sea ice types on Sentinel-1 Synthetic Aperture Radar (SAR) data using a convolutional neural network (CNN) is presented.&#160; The CNN is trained on reference ice charts produced by human experts and compared with an existing machine learning algorithm based on texture features and random forest classifier. The CNN is trained on a dataset from winter 2020 for retrieval of four classes: ice free, young ice, first-year ice and old ice. The accuracy of our classification is 91.6%. The error is a bit higher for young ice (76%) and first-year ice (84%). Our algorithm outperforms the existing random forest product for each ice type. It has also proved to be more efficient in computing time and less sensitive to the noise in SAR data.&#160;Our study demonstrates that CNN can be successfully applied for classification of sea ice types in SAR data. The algorithm is applied in small sub-images extracted from a SAR image after preprocessing including thermal noise removal. Validation shows that the errors are mostly attributed to coarse resolution of ice charts or misclassification of training data by human experts.&#160;Several sensitivity experiments were conducted for testing the impact of CNN architecture, hyperparameters, training parameters and data preprocessing on accuracy. It was shown that a CNN with three convolutional layers, two max-pool layers and three hidden dense layers can be applied to a sub-image with size 50 x 50 pixels for achieving the best results. It was also shown that a CNN can be applied to SAR data without thermal noise removal on the preprocessing step. Understandably, the classification accuracy decreases to 89% but remains reasonable.&#160;The main advantages of the new algorithm are the ability to classify several ice types, higher classification accuracy for each ice type and higher speed of processing than in the previous studies. The relative simplicity of the algorithm (both texture analysis and classification are performed by CNN) is also a benefit. In addition to providing ice type labels, the algorithm also derives the probability of belonging to a class. Uncertainty of the method can be derived from these probabilities and used in the assimilation of ice type in numerical models.&#160; Given the high accuracy and processing speed, the CNN-based algorithm is included in the Copernicus Marine Environment Monitoring Service (CMEMS) for operational sea ice type retrieval for generating ice charts in the Arctic Ocean. It is already released as an open source software and available on Github: https://github.com/nansencenter/s1_icetype_cnn.

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Geostatistical and Statistical Classification of Sea-Ice Properties and Provinces from SAR Data

Remote Sensing ◽

10.3390/rs8080616 ◽

2016 ◽

Vol 8 (8) ◽

pp. 616 ◽

Cited By ~ 3

Author(s):

Ute Herzfeld ◽

Scott Williams ◽

John Heinrichs ◽

James Maslanik ◽

Steven Sucht

Keyword(s):

Sea Ice ◽

Statistical Classification ◽

Sar Data

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Sea-ice type classification from ERS-1 SAR data based on grey level and texture information

Polar Record ◽

10.1017/s0032247400013632 ◽

1995 ◽

Vol 31 (177) ◽

pp. 135-146 ◽

Cited By ~ 11

Author(s):

D.M. Smith ◽

E.C. Barrett ◽

J.C. Scott

Keyword(s):

Sea Ice ◽

Open Water ◽

Speckle Noise ◽

First Year ◽

Grey Level ◽

Texture Information ◽

Texture Parameters ◽

Sar Data ◽

Speckle Noise Reduction

AbstractThis paper describes the development of a practical algorithm for the classification of sea-ice types from ERS-1 synthetic aperture radar (SAR) data. The algorithm was based on a combination of grey level and texture information in order to overcome ambiguous grey level values of different ice types. The problem of calculating texture parameters for windows containing more than one ice type was overcome by first segmenting the image so that only pixels from the same segment were included in the calculation of the texture measure. The segmentation procedure was based on the iterative application of a speckle noise reduction filter, and was thus crucially dependent on the ability of such a filter to smooth out noise without destroying edges and fine features. In order to achieve this, a modification to the sigma filter of Lee (1983b) was developed; it out-performed the sigma filter for a model problem. Two ERS-1 SAR scenes of the marginal ice zone east of Spitsbergen in March 1992 were analysed by calculating values of grey level and range for different ice types contained within raw data extracts. Although the grey levels of some of the ice types overlapped, most of the ambiguity was removed through the additional use of range. It was also necessary to test for the wave-like appearance of open water. The classification scheme was demonstrated to identify correctly most of the grease/new ice, first-year ice, multiyear ice, rough ice, pancake ice, and open water in the two SAR scenes, although there was some misclassification of open water as first-year ice.

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Sea ice classification using wide-swath SAR data considering incidence angle depenency of backscatter intensity and surface texture

10.5194/egusphere-egu2020-11045 ◽

2020 ◽

Author(s):

Wenkai Guo ◽

Polona Itkin ◽

Johannes Philipp Lohse

Keyword(s):

High Resolution ◽

Sea Ice ◽

Surface Texture ◽

Classification Scheme ◽

Incidence Angle ◽

Texture Features ◽

Angle Variation ◽

Backscatter Intensity ◽

Sar Data ◽

Wide Swath

In this study we develop a novel sea ice classification scheme based on remote sensing Synthetic-aperture Radar (SAR) data, and use it to classify sea ice types over the spatial and temporal range of the Norwegian Young sea ICE cruise (N-ICE2015). Ice type classification will be conducted on wide-swath SAR datasets including RADARSAT-2 and Sentinel-1 data. We use a classification scheme that takes into account different rates of decrease in backscatter intensity with incidence angle variation for different classes. In addition, it examines texture features of different sea ice types, and also variations of surface texture with changing incidence angles, and incorporates this relationship into the classification process. Sea ice classifications using high-resolution SAR images collected over the same period and also field data retrieved from the N-ICE2015 expedition will be used for ground truthing. Earlier N-ICE2015 studies with high resolution SAR and deformation suggest high lead and pressure ridge formation. We will use our lower-resolution results to explore potential increase in the fraction of deformed and lead ice from January to June 2015 in the region north of Svalbard.

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Classification of Sea Ice Types in Sentinel-1 SAR Data Using Convolutional Neural Networks

Remote Sensing ◽

10.3390/rs12132165 ◽

2020 ◽

Vol 12 (13) ◽

pp. 2165 ◽

Cited By ~ 2

Author(s):

Hugo Boulze ◽

Anton Korosov ◽

Julien Brajard

Keyword(s):

Random Forest ◽

Sea Ice ◽

Learning Algorithm ◽

Computing Time ◽

Texture Features ◽

Random Forest Classifier ◽

Synthetic Aperture ◽

First Year ◽

Sar Data

A new algorithm for classification of sea ice types on Sentinel-1 Synthetic Aperture Radar (SAR) data using a convolutional neural network (CNN) is presented. The CNN is trained on reference ice charts produced by human experts and compared with an existing machine learning algorithm based on texture features and random forest classifier. The CNN is trained on two datasets in 2018 and 2020 for retrieval of four classes: ice free, young ice, first-year ice and old ice. The accuracy of our classification is 90.5% for the 2018-dataset and 91.6% for the 2020-dataset. The uncertainty is a bit higher for young ice (85%/76% accuracy in 2018/2020) and first-year ice (86%/84% accuracy in 2018/2020). Our algorithm outperforms the existing random forest product for each ice type. It has also proved to be more efficient in computing time and less sensitive to the noise in SAR data. The code is publicly available.

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