AI-Based Remote Sensing Oceanography - Image Classification, Data Fusion, Algorithm Development and Phenomenon Forecast

In the past few years, artificial intelligent (AI) technology has been widely used in many research fields for big data information mining and shown great potential applications in computer vision, natural language processing, bioinformatics, among others. In the area of remote sensing oceanography, we categorize its applications in four major categories: image classification, data fusion, algorithm development and oceanic phenomenon forecast. In this paper, we present two examples to demonstrate such applications. In the first example we applied a well-studied AI framework, U-Net, to a NASA JPL’s UAVSAR Synthetic Aperture Radar (SAR) image to classify coastal zone types in the Gulf Coast of USA. In the second example, we trained the AI framework, LSTM model, using the time series of blended microwave Sea Surface Temperature (SST) data, and made the equatorial SST pattern forecast. Validation studies in both cases showed the robustness of AI-based technology for oceanography research.