Image and Spectrum Based Deep Feature Analysis for Particle Matter Estimation with Weather Informatio
- Resource Type
- Conference
- Authors
- Zhu, Zanbo; Zhao, Ruobing; Ni, Jianyuan; Zhang, Jing
- Source
- 2019 IEEE International Conference on Image Processing (ICIP) Image Processing (ICIP), 2019 IEEE International Conference onhttps://idams.ieee.org/idams/custom/properties/properties.jsp#. :3427-3431 Sep, 2019
- Subject
- Computing and Processing
Signal Processing and Analysis
Feature extraction
Meteorology
Estimation
Air pollution
Data mining
Neural networks
Atmospheric measurements
Deep Features
Particle Matter
Image
Spectrum
Weather Information
- Language
- ISSN
- 2381-8549
Air pollution is a major global risk to human health and environment. Particle matter (PM) with diameters less than 2.5 micrometers (PM 2.5 ) is more harmful to human health than other air pollutants because it can penetrate deeply into lungs and damage human respiratory system. A new imagebased deep feature analysis method is presented in this paper for PM 2.5 concentration estimation. Firstly, low level and high level features are extracted from images and their spectrums by a deep learning neural network, and then regression models are created using the extracted deep features to estimate the PM 2.5 concentrations, which are future refined by the collected weather information. The proposed method was evaluated using a PM 2.5 dataset with 1460 photos and the experimental results demonstrated that our method outperformed other state-of-the-art methods.