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Atmosphere 2018, 9(12), 481; https://doi.org/10.3390/atmos9120481

A Novel Method for the Recognition of Air Visibility Level Based on the Optimal Binary Tree Support Vector Machine

1
College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
2
Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), Nanjing University of Information Science and Technology, Nanjing 210044, China
3
Joint International Research Laboratory of Climate and Environment Change (ILCEC), Nanjing University of Information Science and Technology, Nanjing 210044, China
4
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disaster (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing 210044, China
5
Jiangsu Province Engineering Laboratory of Modern Facility Agriculture Technology and Equipment, Nanjing 210031, China
6
School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
*
Author to whom correspondence should be addressed.
Received: 11 November 2018 / Revised: 2 December 2018 / Accepted: 4 December 2018 / Published: 6 December 2018
(This article belongs to the Section Air Quality)
Full-Text   |   PDF [2238 KB, uploaded 6 December 2018]   |  

Abstract

As the traditional methods for the recognition of air visibility level have the disadvantages of high cost, complicated operation, and the need to set markers, this paper proposes a novel method for the recognition of air visibility level based on an optimal binary tree support vector machine (SVM) using image processing techniques. Firstly, morphological processing is performed on the image. Then, whether the region of interest (ROI) is extracted is determined by the extracted feature values, that is, the contrast features and edge features are extracted in the ROI. After that, the transmittance features of red, green and blue channels (RGB) are extracted throughout the whole image. These feature values are used to construct the visibility level recognition model based on optimal binary tree SVM. The experiments are carried out to verify the proposed method. The experimental results show that the recognition accuracies of the proposed method for four levels of visibility, i.e., good air quality, mild pollution, moderate pollution, and heavy pollution, are 92.00%, 92%, 88.00%, and 100.00%, respectively, with an average recognition accuracy of 93.00%. The proposed method is compared with one-to-one SVM and one-to-many SVM in terms of training time and recognition accuracy. The experimental results show that the proposed method can distinguish four levels of visibility at a relatively satisfactory level, and it performs better than the other two methods in terms of training time and recognition accuracy. This proposed method provides an effective solution for the recognition of air visibility level. View Full-Text
Keywords: air visibility recognition; optimal binary tree; support vector machine; image processing air visibility recognition; optimal binary tree; support vector machine; image processing
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Zheng, N.; Luo, M.; Zou, X.; Qiu, X.; Lu, J.; Han, J.; Wang, S.; Wei, Y.; Zhang, S.; Yao, H. A Novel Method for the Recognition of Air Visibility Level Based on the Optimal Binary Tree Support Vector Machine. Atmosphere 2018, 9, 481.

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