基于机器学习的重庆长江航道雾图像特征识别研究

Identification of the fog image features on the Yangtze River waterways inChongqing based on machine learning

基于重庆市境内长江航道雷达站拍摄的雾天气过程影像资料,利用K最近邻、支持向量机、BP神经网络、随机森林等机器学习算法,对无雾和5类有雾天气个例进行图像识别训练,构建雾图像识别模型,并检验了识别准确率。结果表明:机器学习能够有效识别雾图像,随机森林算法的识别效果优于其余3种算法。对于能见度超过1500 m的无雾天气,模型的识别准确率为100%,对于能见度在1000-1500 m范围内的轻雾、能见度低于50 m的强浓雾,模型的识别准确率在90%以上,对于能见度在50-1000 m范围内的雾、大雾和浓雾,识别准确率超过70%。

Based on the image data of fog events taken by radar stations on the Yangtze River waterways in Chongqing,images without fog events and with five types of fog events were trained using algorithms including K-nearest neighbor,support vector machine,back propagation neural network,and random forest.According to the training results,a fog image identification model was built,and the identification accuracy was tested.The results show that machine learning can effectively identify fog images,and random forest performances better than the other three algorithms.The model has a recognition accuracy of 100%for non-fog recognition,over 90%for light fog and strong dense fog events,and over 70%for fog,heavy fog,and dense fog events.