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Chimney and condensing tower detection based on FPN in high-resolution remote sensing images
Author(s): Qin Deng; Haopeng Zhang
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Paper Abstract

The frequent hazy weather in North China has drawn people's attention. The anthropogenic emission by fossil fuel power plants is one of the main pollution resource, so the environmental protection administration need to monitor power plants. Thus, a power plant detection system is needed to locate power plants and judge their working status. In this paper, we propose a power plant monitoring framework based on Feature Pyramid Network (FPN) to automatically detect the chimneys and condensing towers of the power plants and judge their working status in high resolution remote sensing images (RSIs). We improve the original FPN by changing the number of layers and scales of feature pyramid to get better performance. Experimental results show that our improved FPN framework can effectively detect the chimneys and condensing towers of fossil-fuel power plants and judge their working status with mean average precision up-to 0.8591, showing good potential for power plant monitoring.

Paper Details

Date Published: 7 October 2019
PDF: 7 pages
Proc. SPIE 11155, Image and Signal Processing for Remote Sensing XXV, 111552B (7 October 2019); doi: 10.1117/12.2532376
Show Author Affiliations
Qin Deng, Beihang Univ. (China)
Haopeng Zhang, Beihang Univ. (China)


Published in SPIE Proceedings Vol. 11155:
Image and Signal Processing for Remote Sensing XXV
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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