有效的云检测与云相态判识对于农业、气候及人类生活具有重要意义, 而这些数据的获取离不开卫星遥感。 卫星遥感数据在当今社会的生产和生活中都扮演着至关重要的角色, 众多领域的发展都离不开卫星遥感数据的支持。随着高精度传感器的发展, 传统研究方法无法满足大规模、高维度数据的高效挖掘与处理, 因此深度学习技术在遥感领域得到了快速的发展。基于深度学习技术提出了一种结合多波段遥感影像的云检测及云相态判识的方法。 该方法采用 MODIS 云产品影像作为样本, 将不同波段信息作为特征值, 分别建立针对云检测与云相态判识研究任务的多个数据库, 并采用 DeepLab V3+ 模型进行训练并预测, 从而完成高精度的云检测及云相态判识任务。与传统方法相比, 该方法高效便捷、特征提取能力较强, 将多波段作为特征值输入模型进行预测时, 该方法展现了良好的结果。

Effective cloud detection and cloud phase identification are of great significance to agriculture, climate and human life, and the acquisition of these data is mainly from satellite remote sensing. Satellite remote sensing data plays a vital role in the production and life of current society, and the development of many fields is inseparable from the support of satellite remote sensing data. With the development of high-precision sensors, traditional research methods cannot meet the requirements of large-scale and high-dimensional data mining and processing, so deep learning technology has been rapidly developed in the field of remote sensing. Based on deep learning technology, a method of cloud detection and cloud phase recognition combined with multi-band remote sensing images is proposed in this work. MODIS cloud product images are used as samples, the different waveband information is used as eigenvalue to establish multiple databases for cloud detection and cloud phase state recognition research tasks, and then Deeplab V3+ model is used for training and prediction, so as to complete the high-precision cloud detection and cloud phase state recognition. Compared with the traditional methods, the proposed method is more efficient and convenient, and has stronger feature extraction ability. When multi-band is used as the eigenvalue input model for prediction, the method shows good performance.