Sea Ice Concentration Estimation: Using Passive Microwave and SAR Data With a U-Net and Curriculum Learning

Radhakrishnan, Keerthijan; Scott, K. Andrea; Clausi, David A.

doi:10.1109/jstars.2021.3076109

Cited by 27 publications

(11 citation statements)

References 26 publications

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“…The ASI algorithm produced SIC values close to 0% when the Landsat-8 SIC values less than 20%, i.e., near the ice edge. This is the same result as reported in Radhakrishnan et al [57], presenting that the ASI algorithm tends to underestimate SIC in the vicinity of the ice edge. Meanwhile, the BT algorithm estimated slightly positively biased SIC values.…”

Section: Performance Of Summer Sic Retrieval Model Based On Rf Regressionsupporting

confidence: 91%

Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression

et al. 2021

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The Arctic sea ice concentration (SIC) in summer is a key indicator of global climate change and important information for the development of a more economically valuable Northern Sea Route. Passive microwave (PM) sensors have provided information on the SIC since the 1970s by observing the brightness temperature (TB) of sea ice and open water. However, the SIC in the Arctic estimated by operational algorithms for PM observations is very inaccurate in summer because the TB values of sea ice and open water become similar due to atmospheric effects. In this study, we developed a summer SIC retrieval model for the Pacific Arctic Ocean using Advanced Microwave Scanning Radiometer 2 (AMSR2) observations and European Reanalysis Agency-5 (ERA-5) reanalysis fields based on Random Forest (RF) regression. SIC values computed from the ice/water maps generated from the Korean Multi-purpose Satellite-5 synthetic aperture radar images from July to September in 2015–2017 were used as a reference dataset. A total of 24 features including the TB values of AMSR2 channels, the ratios of TB values (the polarization ratio and the spectral gradient ratio (GR)), total columnar water vapor (TCWV), wind speed, air temperature at 2 m and 925 hPa, and the 30-day average of the air temperatures from the ERA-5 were used as the input variables for the RF model. The RF model showed greatly superior performance in retrieving summer SIC values in the Pacific Arctic Ocean to the Bootstrap (BT) and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithms under various atmospheric conditions. The root mean square error (RMSE) of the RF SIC values was 7.89% compared to the reference SIC values. The BT and ASI SIC values had three times greater values of RMSE (20.19% and 21.39%, respectively) than the RF SIC values. The air temperatures at 2 m and 925 hPa and their 30-day averages, which indicate the ice surface melting conditions, as well as the GR using the vertically polarized channels at 23 GHz and 18 GHz (GR(23V18V)), TCWV, and GR(36V18V), which accounts for atmospheric water content, were identified as the variables that contributed greatly to the RF model. These important variables allowed the RF model to retrieve unbiased and accurate SIC values by taking into account the changes in TB values of sea ice and open water caused by atmospheric effects.

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Section: Performance Of Summer Sic Retrieval Model Based On Rf Regressionsupporting

confidence: 91%

Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression

et al. 2021

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“…de Gelis et al 11 is the first paper to adopt the U-Net CNN architecture 12 in mapping sea ice using SAR data and ice charts as labels. This was closely followed by 13 using SAR data to map SIC with labels retrieved from passive microwave radiometer data while also applying the U-Net architecture and curriculum learning. An alternative branch of sea ice charting, classifying the stage of development of sea ice, has been carried out in 14 .…”

mentioning

confidence: 99%

AI4SeaIce: selecting loss functions for automated SAR sea ice concentration charting

Kucik

Stokholm

2023

Sci Rep

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For maritime navigation in the Arctic, sea ice charts are an essential tool, which still to this day is drawn manually by professional ice analysts. The total Sea Ice Concentration (SIC) is the primary descriptor of the charts and indicates the fraction of ice in an ocean surface area. Naturally, automating the SIC chart creation is desired. However, the optimal representation of the corresponding machine-learning task is ambivalent and discussed in the community. In this study, we explore the representation with either regressional or classification objectives, each with two different (weighted) loss functions: Mean Square Error and Binary Cross-Entropy, and Categorical Cross-Entropy and the Earth Mover’s Distance, respectively. While all models achieve good results they differ as the regression-based models obtain the highest numerical similarity to the reference charts, whereas the classification-optimised models generate results more visually pleasing and consistent. Rescaling the loss functions with inverse class weights improves the performance for intermediate classes at the expense of open water and fully-covered sea ice areas.

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“…Semantic segmentation needs to determine the category label for each pixel in the image and perform accurate segmentation. At present, there are few studies on Arctic Sea ice identification from remote sensing satellite images using semantic segmentation algorithms, 11 – 15 although there are some for ground-based data 16 . Relevant studies use the U-NET or Deeplabv3 model to realize the semantic segmentation of Arctic Sea ice.…”

Section: Introductionmentioning

confidence: 99%

Semantic segmentation of Arctic Sea ice in summer from remote sensing satellite images based on BAU-NET

Ji¹,

Fang

Feng³

et al. 2022

J. Appl. Rem. Sens.

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To effectively solve the accurate identification of gray ice, melt ponds water, floe, brash ice, and thin ice in the melting state of the Arctic Sea ice during summer, we propose adding a batch normalization layer and adaptive moment estimation optimizer of a U-NET (BAU-NET) method for Arctic Sea ice semantic segmentation in summer from remote sensing satellite optical images. The U-NET network structure is optimized to 18 convolution layers, and a batch normalization layer and a nonlinear activation function rectified linear unit are added behind each convolution layer. Then the hyper-parameters of the network structure are adjusted. The cross-entropy loss function based on SOFTMAX and L2 regularization are used in training the model, and the adaptive moment estimation optimizer is used for iterative training until the error convergence. The experimental results show that the accuracy, precision, recall, and F1 score of sea ice extraction results reaches more than 97.26%. Compared with the DeepLabv3 and U-Net methods, the sea ice prediction time efficiency is improved by 90.99s and 1.57s, respectively, and the accuracy is improved by 6.59% and 8.05%, respectively, which indicate that the sea ice prediction time efficiency and accuracy of the BAU-NET method are significantly improved.

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Sea Ice Concentration Estimation: Using Passive Microwave and SAR Data With a U-Net and Curriculum Learning

Cited by 27 publications

References 26 publications

Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression

Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression

AI4SeaIce: selecting loss functions for automated SAR sea ice concentration charting

Semantic segmentation of Arctic Sea ice in summer from remote sensing satellite images based on BAU-NET

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