Domain Adaptation on Multiple Cloud Recognition From Different Types of Meteorological Satellite - Huang, Bin; Xiao, Luming; Feng, Wen; Xu, Mengqiu; Wu, Ming; Fang, Xiang (Frontiers in Earth Science, 2022)
At present, meteorological satellite has become an indispensable meteorological tool for earth observation, such as cloud detection, that which has important guiding significance for maritime activities. However, it is time-consuming and labor-intensive to obtain fine-grained annotations provided by artificial experience or mature satellite cloud products for multi-spectral maritime cloud imageries, especially when some new satellites launched. Moreover, due to the data discrepancy caused by different detection bands, existing models have inadequate generalization performance on new satellites, and some can’t be directly migrated. In this paper, to reduce the data distribution’s discrepancy, an approach is presented based on unsupervised domain adaption method for marine cloud detection task based on Himawari-8 satellite data as source domain and Fengyun-4 satellite data as target domain. The goal of proposed method is to leverage the representation power of adversarial learning to extract domain-invariant features, consisting of a segmentation model, a feature extract model for target domain and a domain discriminator. In addition, aiming to the discrepancy of detection bands, a band mapping module is designed to implement consistency of different bands. The result of the experiments demonstrated the effectiveness of the proposed method with a 7% improvement compared with the comparative experiment. Besides, we designed a series of statistical experiments on different satellite data to study further cloudy perception representation, including data visualization experiment, cloud type statistics, etc.