Yu, H.F.; Wang, C.Y.; Sui, Y.; Li, J.H., and Chu, J.L., 2020. Automatic extraction of green tide using dual polarization China GF-3 SAR images. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T.W. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 318-325. Coconut Creek (Florida), ISSN 0749-0208.
Traditional optical images are not suitable for the all-weather observation of green tide due to the fact that they are greatly affected by clouds. Synthetic aperture radar (SAR) on the other hand, is able to realize all-day and all-weather observation due to a lower sensitivity to clouds, rain and fog. SAR images are therefore an effective supplementary data for the monitoring of green tide. Due to the differences in brightness and noise across the different areas in an image, it is difficult to use the same threshold in order to extract all the green tide information from the image. Based on the iterative threshold method and the histogram bimodal method, this paper presents a new method for the automatic detection of green tide that uses adaptive thresholds for Gaofen-3 (GF-3) satellite dual polarization SAR remote sensing images. In this study, firstly, a sliding window is used to segment the image into multiple sub-images of the same size; secondly, the iterative threshold method is used to obtain the green tide and seawater samples that have the theoretical bimodal structure from each sub-image; then, using the histogram bimodal method, the detection threshold is calculated automatically; and finally, using the threshold segmentation, the green tide areas are extracted from the images. In order to verify the proposed method, the multi-scale segmentation method and the proposed method are both used to detect green tide in China Yellow Sea. The study results show that in the case of GF-3 SAR FSII images, the proposed automatic detection method is superior to the multi-scale segmentation method, as it not only improves the accuracy of green tide detection, but also realizes the automation of green tide extraction. Furthermore, Cross-polarization (HV) images may be more suitable than co-polarization (HH) images for the extraction of green tide due to their lower noise level.