It is of considerable benefit to combine information obtained from different satellite sensors to achieve advanced and improved characterization of sea ice conditions. However, it is also true that not all the information is relevant. It may be redundant, corrupted, or unnecessary for the given task, hence decreasing the performance of the algorithms. Therefore, it is crucial to select an optimal set of image attributes that provides the relevant information content to enhance the efficiency and accuracy of the image interpretation and retrieval of geophysical parameters. Comprehensive studies have been focused on the analysis of relevant features for sea ice analysis obtained from different sensors, especially synthetic aperture radar. However, the outcomes of these studies are mostly data and applicationdependent, and can therefore rarely be generalized. In this article, we employ a feature selection method based on Graph Laplacians, which is fully automatic and easy to implement. The proposed approach assesses relevant information on a global and local level using two metrics and selects relevant features for different regions of an image according to their physical characteristics and observation conditions. In the recent study we investigate the effectiveness of this approach for sea ice classification, using different multi-sensor data combinations. Experiments show the advantage of applying multi-sensor data sets and demonstrate that the attributes selected by our method result in high classification accuracies. We demonstrate that our approach automatically considers varying technical, sensorspecific, environmental, and sea ice conditions by employing flexible and adaptive feature selection method as a pre-processing step.

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The automatic detection and analysis of ocean eddies in the marginal ice zone via remote sensing is a very challenging task but of critical importance for scientific applications and anthropogenic activities. Therefore, as one of the first steps toward the automation of the eddy detection process, we investigated the potential of applying YOLOv5, a deep convolutional neural network architecture, to specifically collected and labeled high-resolution synthetic aperture radar data for a very dynamic area over the Fram Strait. Our approach involved fine-tuning pre-trained YOLOv5 models on a sparse dataset and achieved accurate results with minimal training data. The performances of the models were evaluated using several metrics, and the best model was selected by visual examination. The experimental results obtained from the validation and test datasets consistently demonstrated the robustness and effectiveness of the chosen model to identify submesoscale and mesoscale eddies with different structures. Moreover, our work provides a foundation for automated eddy detection in the marginal ice zone using synthetic aperture radar imagery and contributes to advancing oceanography research.

The detection and analysis of ocean eddies via remote sensing have become a hot topic in physical oceanography during the last few decades. However, eddy identification and tracking via remote sensing can be a challenging task since each sensor has some limitations. In order to overcome potential challenges, it is crucial to exploit the complementary information provided by different sensing systems. As one of the steps towards this aim, we have investigated the pertinence of applying the scheme including texture features extraction and superpixel segmentation method in order to distinguish eddies in the marginal ice zone using multisensor remote sensing data. Nevertheless not all the images available from various sensors are of actual importance since they can be corrupted, redundant, or simply unnecessary for a particular task. Therefore, we are additionally exploring the relevance of different sensors separately and simultaneously as well as with extracted texture features for eddy monitoring.

Для организации наставнических сессий серебряные наставники используют тьюторские технологии и технологии открытого образования, в том числе – развития критического мышления. В статье сделана попытка обоснования применения данной технологии в наставнической деятельности, представлены технологические стратегии и приѐмы, направленные на становление навыков self skills.