基于图像理解的能见度测量方法

Visibility Measurement with Image Understanding

现存的大气能见度测量方法主要存在硬件成本高、操作复杂度大、应用范围小等问题.文中将机器学习引入能见度测量,提出一种基于图像理解的白天能见度测量方法.首先在对测量场景图像感兴趣区域分窗处理的基础上,设计并提取基于像素对比度的图像特征及向量.然后,通过训练支持向量回归机构建图像特征与能见度值之间的关系模型.最后,根据模型计算待测图像的能见度值.实验结果表明该方法不仅具有良好的能见度测量精度,同时具有较高的灵活性,能降低已有能见度测量的局限性.

High hardware cost, complex operation and narrow application are main problems of existing measuring methods of atmospheric visibility. In this paper, machine learning is introduced into the study of visibility measurement and a method of daytime visibility measurement is proposed based on image understanding. Firstly, image features and vectors based on pixel contrast are designed and extracted grounded on the segmentation of the regions of interest in measured scene images. Then, the relational model between image features and visibility is constructed by training support vector regression. Finally, visibility of images to be measured is computed according to the model. Experimental results show that the proposed method has both high visibility measuring precision and good flexibility. Moreover, it reduces the limitations of existing visibility measurement methods.