Extremely efficient PM2.5 estimator based on analysis of saliency and statistics. Issue 1 (1st January 2019) - Zhang, Huiqing; Peng, Du; Chen, Weiling; Xu, Xin
Abstract : Air pollution is a crucial environmental problem, especially the fine particulate matter (PM2.5) which has become one of the focal points. PM2.5 is a complex pollutant which can intrude the lungs and threaten people's health during the whole lives. In order to enable people to know the PM2.5 index of their surroundings at any time, an image‐based PM2.5 predictor with saliency detection (IPPS) is proposed. The proposed predictor first obtains the non‐salient regions based on saliency detection technologies. Then, the authors extract two features of the entropy and intensity values of non‐salient image saturation map. Finally, they multiply these two features into the approximation of PM2.5 concentration. Experiments show that the proposed IPPS is superior in accuracy and efficiency.