【摘要】 随着科学技术的发展，对地基云的研究也越来越深入，地基云的研究对天气预报、水资源管理、农业生产等领域具有重要的意义。传统地基云分类方法存在数据需求大、运行速率慢等问题。研究为了解决这些问题构建了一种融合双通道卷积神经网络(ConvolutionalNeuralNetworks, CNN)算法与压缩感知的地基云分类器。首先通过对CNN进行改进，得到双通道的CNN算法，然后将其与压缩感知进行融合，得到地基云人工智能分类器；最后通过不同方法进行对，以验证构建的地基云分类器对地基云的分类能力。结果表明在光线正常、光线较暗、水平角度、俯仰角度的观测前提下，地基云分类器的识别准确率平均值为73.95%、45.39%、92.61%和43.82%,均高于对照算法。这表明该地基云分类器具有较高的准确率和鲁棒性。更多还原

【Abstract】 With the development of science and technology, the research on ground-based clouds has become more and more in-depth, and the research on ground-based clouds is important for weather forecasting, water resources management, agricultural production and other fields. Traditional ground-based cloud classification methods have problems such as large data requirements and slow operation rates. The study constructs a ground-based cloud classifier that combines a two-channel Convolutional Neural Networks(CNN) algorithm with compression-awareness in order to solve these problems. Firstly, a dual-channel CNN algorithm is obtained by improving the CNN, and then it is fused with compressive sensing to obtain a ground-based cloud artificial intelligence classifier; finally, a pair of different methods is conducted to verify the classification ability of the constructed ground-based cloud classifier for ground-based clouds. The results show that the average recognition accuracy of the ground-based cloud classifier is 73.95%, 45.39%, 92.61% and 43.82% under the observation premise of normal light, low light, horizontal angle and pitch angle, which are higher than the control algorithm. This indicates that this ground-based cloud classifier has high accuracy and robustness.更多还原