Feature Extraction of Oceanic Internal Waves Based on Remote Sensing Imagery

Oceanic internal waves play an important role in fishing activities and the safety of oil drilling platforms, as well as the navy’s antisubmarine warfare (ASW) and the safety of submarines. Due to its characteristics of relatively all weather capability, Synthetic Aperture Radar (SAR) images became popular in observing the appearance of oceanic internal waves recently. The purpose of this study is to develop a methodology and a computer assisted tool to extract features of internal waves based on SAR imagery using spatial information techniques including digital image processing, wavelet transform, and geographic information analysis. Among the methods of feature extraction, edge detection will be the primary method for extracting features of oceanic internal waves. Thus, methods of Canny, WTMM (Wavelet Transform Modulus Maxima), and Wavelet Correlator will be implemented and investigated for selecting a proper edge detector. The developed tool will then be applied to extract features of the internal waves in the northern South China Sea. Integrating the extracted features and the in-situ observations in a geographic information system (GIS), the dynamics of the internal waves can be better understood. The automated process of extracting features of oceanic internal waves show significant time saving compared to the traditional manual operation.