Air pollution forecasting from sky images with shallow and deep classifiers

作者：Vahdatpour Mohammad Saleh; Sajedi Hedieh^*; Ramezani Farzad

来源：Earth Science Informatics, 2018, 11(3): 413-422.

DOI：10.1007/s12145-018-0334-x

• Air pollution
• Image processing
• Gabor filter
• Statistical moments
• Convolutional Neural Network

Air pollution is one of the most important problems in the new era. Detecting the level of air pollution from an image taken by a camera can be informative for the people who are not aware of exact air pollution level be declared daily by some organizations like municipalities. In this paper, we propose a method to predict the level of the air pollution of a location by taking an image by a camera of a smart phone then processing it. We collected an image dataset from city of Tehran. Afterward, we proposed two methods for estimation of level of air pollution. In the first method, the images are preprocessed and then Gabor transform is used to extract features from the images. At the end, two shallow classification methods are employed to model and predict the level of air pollution. In the second proposed method, a Convolutional Neural Network(CNN) is designed to receive a sky image as an input and result a level of air pollution. Some experiments have been done to evaluate the proposed method. The results show that the proposed 9 method has an acceptable accuracy in detection of the air pollution level. Our deep classifier achieved accuracy about 59.38% which is 10 about 6% higher than traditional combination of feature extraction and classification methods.

• 出版日期2018-9