基于深度学习的地基全景云图云状自动分类技术研究

Cloud plays an important role in atmospheric radiative transfer and climate change. The traditional ground-based cloud recognition depends on the artificial experience to extract features, therefore the effectiveness for all kinds cloud of the features is limited, Without consideration of the dynamic and associating features, moreover, the feature extraction and classification are two aspects separately, these factors greatly affect the recognition accuracy and speed, do not realize the true meaning of intelligent automation. Deep learning (DL) can learn the features of stronger representation, and it can effectively improve the accuracy of classification by applying to the ground-based cloud classification. This work focuses on the new theory and new technology of ground-based cloud recognition based on deep learning, 1) we detailed analysis the key technologies of three types deep learning networks of cloud recognition, including the network model structure, parameter selection and learning algorithm, 2) we establish a network model with high precision which suit for cloud classification, 3) we research the processing algorithm on small sample. Through the experiment to test the feasibility and effectiveness of automatic cloud classification based on deep learning, we provide the new methods and means for the and ground-based cloud classification, and provide the scientific basis to describe the changes in weather and climate with qualitative and quantitative.

云在大气辐射传输和气候变化中起着重要作用。目前常规的地基云状识别是基于人工经验提取特征，提取的特征适用云类有限，未考虑到动态特征及伴随特征，且特征提取与分类识别是分开的两个环节，很大程度上影响了识别精度和速度，未实现真正意义上的自动化。深度学习可以提取到表征能力更强的特征，将深度学习应用到地基云状的分类中，可以有效地提高分类的精度。本项目着重研究基于深度学习的地基云状识别新技术，1）详细分析地基云识别深度学习网络中涉及的建模、模型结构、参数选择和学习算法等关键技术，2）建立适用于地基云状自动识别的高精度深度学习网络模型，3）研究辅助特征及小样本深度学习地基云状识别处理算法。通过试验验证基于深度学习的地基云状自动识别方法的可行性和有效性，为地基云图云状自动识别提供新的方法和手段，为定量定性描述天气及气候的变化情况提供新技术。

云的形成和演变是大气中物理过程的宏观体现，地面云观测在天气现象的观测、记录和研究中占有重要地位，现有方法大多数采用手工制作视觉描述符，其结果不十分令人满意。受深度学习在大规模图像分类领域中取得巨大成功的启发，本项目通过对深度学习技术在地基云识别中的实验评估以及深度学习关键技术深入研究，搜集国内外标准地基云图数据集以及网络现有的地基云图，经整理和预处理后，建立一套统一的符合国际标准的地基云图数据库；研究了适用于地基云图数据库的多个自适应识别算法模型，并研发相应的地基云图自适应识别软件系统。研究成果涉及理论模型和示范应用软件，发表多篇高水平论文、出版1部科学专著，申请并授权多项专利，为地基云图云状的智能化和自动化识别提供新的方法和手段，为定量定性描述天气及气候的变化情况提供新技术。