The paper presents the results of the first version algorithm development for sea ice binary classification by SAR images. To create training, test, and validation samples, we have used 81 images acquired with the Sentinel-1 radar for the area of the Pechora Sea (the south-western part of the Barents Sea) for the 2019–2020 ice period. We conducted the preprocessing procedure for each image aimed at better image quality, noise removal, including gradient noise, and geospatial reference. The marking images was carried out semi-automatically using the K-means clustering algorithm. The result of clustering is a bitmap file with a class number assigned to each pixel. The raster was then vectorized and the expert manually divided the resulting vector polygons into water and ice classes. Validation images were monitored using a set of metrics with the following average result achieved: 0.86 (Jaccard), 0.14 (Binary Crossentropy), 0.90 (Precision), 0.95 (Recall). Expert analysis of binary classification errors has shown that they are typical for the periods when ice is being actively formed or destructed, which results in alternating small areas of ice and open water offshore.
Unsupervised change detection method in SAR images based on deep belief network using an improved fuzzy C‐means clustering algorithm