Improved Sea Ice Image Segmentation Using U 2 -Net and Dataset Augmentation

• Published in: Applied Sciences
• Main Authors: Yongjian Li, He Li, Dazhao Fan, Zhixin Li, Song Ji
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2023
• Subjects: sea ice segmentation; U 2 -Net; remote sensing images; Technology; T; Engineering (General). Civil engineering (General); TA1-2040; Biology (General); QH301-705.5; Physics; QC1-999; Chemistry; QD1-999; Sea ice
• Online Access: https://doi.org/10.3390/app13169402; https://doaj.org/article/50fdd993dd7a43b7b2125ce697d0e8b5

Sea ice extraction and segmentation of remote sensing images is the basis for sea ice monitoring. Traditional image segmentation methods rely on manual sampling and require complex feature extraction. Deep-learning-based semantic segmentation methods have the advantages of high efficiency, intelligence, and automation. Sea ice segmentation using deep learning methods faces the following problems: in terms of datasets, the high cost of sea ice image label production leads to fewer datasets for sea ice segmentation; in terms of image quality, remote sensing image noise and severe weather conditions affect image quality, which affects the accuracy of sea ice extraction. To address the quantity and quality of the dataset, this study used multiple data augmentation methods for data expansion. To improve the semantic segmentation accuracy, the SC-U 2 -Net network was constructed using multiscale inflation convolution and a multilayer convolutional block attention module (CBAM) attention mechanism for the U 2 -Net network. The experiments showed that (1) data augmentation solved the problem of an insufficient number of training samples to a certain extent and improved the accuracy of image segmentation; (2) this study designed a multilevel Gaussian noise data augmentation scheme to improve the network’s ability to resist noise interference and achieve a more accurate segmentation of images with different degrees of noise pollution; (3) the inclusion of a multiscale inflation perceptron and multilayer CBAM attention mechanism improved the ability of U 2 -Net network feature extraction and enhanced the model accuracy and generalization ability.