2003/01/01 by S. Y. Wu, Shuying Wu, A. K. Liu · 1 citation
Earth and Planetary Sciences · Environmental Science · #Geological and Geophysical Studies #Methane Hydrates and Related Phenomena #Oceanographic and Atmospheric Processes
paper · doi:10.1080/01431160210144606
openalex publication_date 2003/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
Spaceborne synthetic aperture radar (SAR) observation is an important tool for monitoring and studying changes in various geophysical elements in and above world oceans. Because of SAR's ideal imaging capability and high resolution, the collection of SAR data will likely extend well into the 21st century. As the data become increasingly abundant and computers faster and more affordable, it naturally leads to an increasing need for an automated procedure to replace the labour-intensive manual screening process. In this paper, an integrated scheme for detection, extraction and classification of linear ocean features in SAR imagery is attempted for the purpose of automated screening. The methodology consists of feature detection based on greyscale histogram screening, feature extraction based on two-dimensional wavelet analysis and feature classification based on texture analysis. Using these algorithms on SAR data, several case studies of linear ocean features, including fronts, ice edges and a polar low, are presented herein. Though not fully automated at this stage, the integration of these algorithms seems to lay a promising foundation for the future development of a more automated ocean feature detection, extraction and classification scheme.