摘要
针对单张图像的传统天气识别方法准确率较低,以及对天空区域不明显的图像具有识别局限性的问题,提出基于分区域特征提取的天气识别方法;在含有晴天、多云、阴天、雾天4种天气的数据集中,对图像进行天际线分割,并识别天空区域与地面区域;分别提取图像中天空区域、地面区域和图像整体的纹理特征、形状特征及颜色特征,并利用随机森林分类模型进行训练和测试。结果表明,该方法对于单张图像天气识别的准确率为92.6%,可以准确地识别图像天气,具有较强的实用性和普适性。
Aiming at the problem that traditional weather recognition methods for single image had lower accuracies and recognition limitation for images without obvious sky region,a novel weather recognition method based on feature extraction in different regions was proposed.In a data set with four kinds of weather including clear sky,cloudy,overcast,and foggy days,the skyline of the image was segmented,and the sky area as well as the ground area were identified.Textural features,shape features and color features of the sky area,the ground area and the whole image were extracted respectively,which were trained and tested by using the random forest classification model.The results show that the accuracy of this method in single image weather recognition is 92.6%.It can recognize image weather accurately,and has stronger practicability and universality.
作者
李鹏程
吕昌峰
于向茹
李金屏
LI Pengcheng;LYU Changfeng;YU Xiangru;LI Jinping(School of Information Science and Engineering,University of Jinan,Jinan 250022,Shandong,China;Shandong Provincial Key Laboratory of Network Based Intelligent Computing,University of Jinan,Jinan 250022,Shandong,China;Shandong College and University Key Laboratory of Information Processing and Cognitive Computing in 13th Five-year,University of Jinan,Jinan 250022,Shandong,China;Shandong Senter Electronic Company Limited,Zibo 255088,Shandong,China)
出处
《济南大学学报(自然科学版)》
CAS
北大核心
2020年第4期321-327,共7页
Journal of University of Jinan(Science and Technology)
基金
国家自然科学基金项目(61701192)
山东省重点研发计划项目(2017CXGC0810)
山东省教育科学规划“教育招生考试科学研究专设课题”(ZK1337212B008)。
关键词
图像天气识别
天际线分割
特征提取
随机森林
weather classification of image
skyline segmentation
feature extraction
random forest