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Based on 2 RADARSAT-2 SAR (Synthetic Aperture Radar) SLC (Single Look Complex) data and the cross-spectrum method, the distributions of significant wave heights and mean wave periods of ocean wave were inverted, and the distributions of the wave power density, which can represent the wave power, were calculated. Some comparisons between the results of the SAR and those of the ECMWF (European Center for Medium-Range Weather Forecasts) dataset were carried out, and the observation ability for the distribution characteristics of wave and wave power by the SAR was studied in detail. The results show that compared with buoys, which are single-point observations, and wave models, which are simulated observations, the SAR is an ideal tool for extensive wave observations in nearshore waters. The advantage of SAR observations is that some fine structure and microcosmic changes in wave and wave power can be found, which helps us understand the internal structure and transformation patterns of wave better. Therefore, the SAR showed extremely extensive application prospects in the field of wave observations. The contributions in this paper will provide some references for researchers aiming for ocean wave detection by remote sensing and promote the application level of SAR in the detection of wave and wave power. [ABSTRACT FROM AUTHOR]

Baek, W.-K.; Jung, H.-S.; and Kim, D., 2020. Oil spill detection of Kerch strait in November 2007 from dual-polarized TerraSAR-X image using artificial and convolutional neural network regression models. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 137-144. Coconut Creek (Florida), ISSN 0749-0208. The oil spill is the main marine disaster. It is known that the data mining based method performs better in detecting oil than the traditional SAR based method to distinguish from lookalikes. Recently, artificial neural networks were employed to detect the oil spill. For that synthetic aperture radar intensity map, intensity texture map, co-polarized coherence map, co-polarized phase difference texture map, and digital elevation model was employed as input. The detection performance based on data mining largely depends on the used data, the characteristics of the study area, and the used model. Improving detection performance through various existing data mining models is a very important task since minor improvements in detection can have a significant impact on disaster damage mitigation. Artificial and convolutional neural network regression models were applied to detect the oil spill of Kerch strait in November 2007. The two models showed F1-scores of 0.832 and 0.823 respectively. The results of this study would contribute to monitor oil spill dynamic and mitigate damages caused by the oil spill. [ABSTRACT FROM AUTHOR]