scholarly journals A Two-Round Weight Voting Strategy-Based Ensemble Learning Method for Sea Ice Classification of Sentinel-1 Imagery

2021 ◽  
Vol 13 (19) ◽  
pp. 3945
Author(s):  
Bin Wang ◽  
Linghui Xia ◽  
Dongmei Song ◽  
Zhongwei Li ◽  
Ning Wang
Keyword(s):  
Sea Ice ◽  
Ensemble Learning ◽  
Classification Systems ◽  
The Arctic ◽  
Local Similarity ◽  
Learning Method ◽  
Classification Result ◽  
Ship Navigation ◽  
Voting Strategy

Sea ice information in the Arctic region is essential for climatic change monitoring and ship navigation. Although many sea ice classification methods have been put forward, the accuracy and usability of classification systems can still be improved. In this paper, a two-round weight voting strategy-based ensemble learning method is proposed for refining sea ice classification. The proposed method includes three main steps. (1) The preferable features of sea ice are constituted by polarization features (HH, HV, HH/HV) and the top six GLCM-derived texture features via a random forest. (2) The initial classification maps can then be generated by an ensemble learning method, which includes six base classifiers (NB, DT, KNN, LR, ANN, and SVM). The tuned voting weights by a genetic algorithm are employed to obtain the category score matrix and, further, the first coarse classification result. (3) Some pixels may be misclassified due to their corresponding numerically close score value. By introducing an experiential score threshold, each pixel is identified as a fuzzy or an explicit pixel. The fuzzy pixels can then be further rectified based on the local similarity of the neighboring explicit pixels, thereby yielding the final precise classification result. The proposed method was examined on 18 Sentinel-1 EW images, which were captured in the Northeast Passage from November 2019 to April 2020. The experiments show that the proposed method can effectively maintain the edge profile of sea ice and restrain noise from SAR. It is superior to the current mainstream ensemble learning algorithms with the overall accuracy reaching 97%. The main contribution of this study is proposing a superior weight voting strategy in the ensemble learning method for sea ice classification of Sentinel-1 imagery, which is of great significance for guiding secure ship navigation and ice hazard forecasting in winter.

Impact of Sea Ice Compression on Navigation Performance

2017 ◽  
Author(s):  
Genki Sagawa
Keyword(s):  
Numerical Model ◽  
Sea Ice ◽  
Static Condition ◽  
Performance Estimation ◽  
The Arctic ◽  
Dynamical Processes ◽  
Speed Reduction ◽  
Ship Navigation ◽  
Arctic Ice ◽  
Ice Navigation

Ship navigation performance in the Arctic ice-covered sea was investigated from various kinds of satellite data and a numerical model of sea ice. The effect of dynamical processes of ice on the performance was especially examined, for it was not focused enough in previous studies. As a result, it was found that ice stress can explain some parts of the navigation when high amount of speed reduction occurred in thin ice area, and vice versa. The result indicates an importance of considering dynamical processes of ice in addition to static condition of ice, to improve an accuracy of an ice navigation performance estimation.

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

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

<p>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 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.</p><p> </p><p>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.</p><p> </p><p>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.</p><p> </p><p>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. </p><p><br>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.</p>

scholarly journals Lead detection in Arctic sea ice from CryoSat-2: quality assessment, lead area fraction and width distribution

2015 ◽  
Vol 9 (5) ◽  
pp. 1955-1968 ◽  
Author(s):  
A. Wernecke ◽  
L. Kaleschke
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Maximum Power ◽  
Area Fraction ◽  
The Arctic ◽  
Regional Study ◽  
Arctic Sea ◽  
Width Distribution ◽  
Spot Image

Abstract. Leads cover only a small fraction of the Arctic sea ice but they have a dominant effect on the turbulent exchange between the ocean and the atmosphere. A supervised classification of CryoSat-2 measurements is performed by a comparison with visual MODIS scenes. For several parameters thresholds are optimized and tested in order to reproduce this prior classification. The maximum power of the waveform shows the best classification properties amongst them, including the pulse peakiness. The sea surface height is derived and its spread is clearly reduced for a classifier based on the maximum power compared to published ones. Lead area fraction estimates based on CryoSat-2 show a major fracturing event in the Beaufort Sea in 2013. The resulting Arctic-wide lead width distribution follows a power law with an exponent of 2.47 ± 0.04 for the winter seasons from 2011 to 2014, confirming and complementing a regional study based on a high-resolution SPOT image.

scholarly journals Different Approaches to Reducing Bias in Classification of Medical Data by Ensemble Learning Methods

2021 ◽  
Vol 6 (2) ◽  
pp. 15-30
Author(s):  
Adem Doganer
Keyword(s):  
Sample Size ◽  
Ensemble Learning ◽  
Classification Performance ◽  
Medical Data ◽  
Bias Error ◽  
Learning Method ◽  
Sample Sizes ◽  
Learning Methods ◽  
Learning Performances

In this study, different models were created to reduce bias by ensemble learning methods. Reducing the bias error will improve the classification performance. In order to increase the classification performance, the most appropriate ensemble learning method and ideal sample size were investigated. Bias values and learning performances of different ensemble learning methods were compared. AdaBoost ensemble learning method provided the lowest bias value with n: 250 sample size while Stacking ensemble learning method provided the lowest bias value with n: 500, n: 750, n: 1000, n: 2000, n: 4000, n: 6000, n: 8000, n: 10000, and n: 20000 sample sizes. When the learning performances were compared, AdaBoost ensemble learning method and RBF classifier achieved the best performance with n: 250 sample size (ACC = 0.956, AUC: 0.987). The AdaBoost ensemble learning method and REPTree classifier achieved the best performance with n: 20000 sample size (ACC = 0.990, AUC = 0.999). In conclusion, for reduction of bias, methods based on stacking displayed a higher performance compared to other methods.

scholarly journals Classification of Sea Ice Types in the Arctic by Radar Echoes from SARAL/AltiKa

2021 ◽  
Vol 13 (16) ◽  
pp. 3183
Author(s):  
Renée Mie Fredensborg Hansen ◽  
Eero Rinne ◽  
Henriette Skourup
Keyword(s):  
Sea Ice ◽  
Leading Edge ◽  
Open Water ◽  
Classification Performance ◽  
The Arctic ◽  
Classification Algorithms ◽  
Dual Frequency ◽  
Ka Band ◽  
Ku Band

An important step in the sea ice freeboard to thickness conversion is the classification of sea ice types, since the ice type affects the snow depth and ice density. Studies using Ku-band CryoSat-2 have shown promise in distinguishing FYI and MYI based on the parametrisation of the radar echo. Here, we investigate applying the same classification algorithms that have shown success for Ku-band measurements to measurements acquired by SARAL/AltiKa at the Ka-band. Four different classifiers are investigated, i.e., the threshold-based, Bayesian, Random Forest (RF) and k-nearest neighbour (KNN), by using data from five 35 day cycles during Arctic mid-winter in 2014–2018. The overall classification performance shows the highest accuracy of 93% for FYI (Bayesian classifier) and 39% for MYI (threshold-based classifier). For all classification algorithms, more than half of the MYI cover falsely classifies as FYI, showing the difference in the surface characteristics attainable by Ka-band compared to Ku-band due to different scattering mechanisms. However, high overall classification performance (above 90%) is estimated for FYI for three supervised classifiers (KNN, RF and Bayesian). Furthermore, the leading-edge width parameter shows potential in discriminating open water (ocean) and sea ice when visually compared with reference data. Our results encourage the use of waveform parameters in the further validation of sea ice/open water edges and discrimination of sea ice types combining Ka- and Ku-band, especially with the planned launch of the dual-frequency altimeter mission Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) in 2027.

Extreme Sea Ice Loss over the Arctic: An Analysis Based on Anomalous Moisture Transport

2016 ◽  
Author(s):  
Marta Vázquez ◽  
Raquel Nieto ◽  
Anita Drumond ◽  
Luis Gimeno
Keyword(s):  
Sea Ice ◽  
Moisture Transport ◽  
The Arctic

Classification of the capitalization strategy of the arctic mineral-raw resource rent

2018 ◽  
pp. 26-30
Author(s):  
Yu.V. Razovsky ◽  
◽  
M.S. Ruban ◽  
E.Yu. Gorenkova ◽  
◽  
...  
Keyword(s):  
The Arctic ◽  
Resource Rent

Investigations of past climate and sea-ice variability in the fjord area by Station Nord, eastern North Greenland

1969 ◽  
Vol 35 ◽  
pp. 67-70 ◽  
Author(s):  
Niels Nørgaard-Pedersen ◽  
Sofia Ribeiro ◽  
Naja Mikkelsen ◽  
Audrey Limoges ◽  
Marit-Solveig Seidenkrantz
Keyword(s):  
Sea Ice ◽  
Outer Part ◽  
Late Summer ◽  
Field Work ◽  
The Arctic ◽  
Research Station ◽  
Fjord System ◽  
Sea Bottom ◽  
Satellite Radar ◽  
North Greenland

The marine record of the Independence–Danmark fjord system extending out to the Wandel Hav in eastern North Greenland (Fig. 1A) is little known due to the almost perennial sea-ice cover, which makes the region inaccessible for research vessels (Nørgaard-Pedersen et al. 2008), and only a few depth measurements have been conducted in the area. In 2015, the Villum Research Station, a new logistic base for scientific investigations, was opened at Station Nord. In contrast to the early exploration of the region, it is now possible to observe and track the seasonal character and changes of ice in the fjord system and the Arctic Ocean through remote sensing by satellite radar systems. Satellite data going back to the early 1980s show that the outer part of the Independence–Danmark fjord system is characterised by perennial sea ice whereas both the southern part of the fjord system and an area 20–30 km west of Station Nord are partly ice free during late summer (Fig. 1B). Hence, marine-orientated field work can be conducted from the sea ice using snow mobiles, and by drilling through the ice to reach the underlying water and sea bottom.

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