Automatic and Accurate Extraction of Sea Ice in the Turbid Waters of the Yellow River Estuary Based on Image Spectral and Spatial Information

• Published in: Remote Sensing
• Main Authors: Huachang Qiu, Zhaoning Gong, Kuinan Mou, Jianfang Hu, Yinghai Ke, Demin Zhou
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2022
• Subjects: Yellow River Estuary; turbid area; spectral information; textural features; sea ice extension; automatic extraction; Science; Q; Sea ice
• Online Access: https://doi.org/10.3390/rs14040927; https://doaj.org/article/acc282e394ab474c9277ea064b6fcc8c

Sea ice is an important part of the global cryosphere and an important variable in the global climate system. Sea ice also presents one of the major natural disasters in the world. The automatic and accurate extraction of sea ice extent is of great significance for the study of climate change and disaster prevention. The accuracy of sea ice extraction in the Yellow River Estuary is low due to the large dynamic changes in the suspended particulate matter (SPM). In this study, a set of sea ice automatic extraction method systems combining image spectral information and textural information is developed. First, a sea ice spectral information index that can adapt to sea areas with different turbidity levels is developed to mine the spectral information of different types of sea ice. In addition, the image’s textural feature parameters and edge point density map are extracted to mine the spatial information concerning the sea ice. Then, multi-scale segmentation is performed on the image. Finally, the OTSU algorithm is used to determine the threshold to achieve automatic sea ice extraction. The method was successfully applied to Gaofen-1 (GF1), Sentinel-2, and Landsat 8 images, where the extraction accuracy of sea ice was over 93%, which was more than 5% higher than that of SVM and K-Means. At the same time, the method was applied to the Liaodong Bay area, and the extraction accuracy reached 99%. These findings reveal that the method exhibits good reliability and robustness.