Next Article in Journal
Coastal Remote Sensing: Merging Physical, Chemical, and Biological Data as Tailings Drift onto Buffalo Reef, Lake Superior
Previous Article in Journal
A Nowcast/Forecast System for Japan’s Coasts Using Daily Assimilation of Remote Sensing and In Situ Data

Atmospheric Light Estimation Based Remote Sensing Image Dehazing

College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
College of Computer Science, Chongqing University, Chongqing 400044, China
Computer Information Systems Department, State University of New York at Buffalo State, Buffalo, NY 14222, USA
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Chongqing Geomatics and Remote Sensing Center, Chongqing 401147, China
Author to whom correspondence should be addressed.
Academic Editor: Andrea Garzelli
Remote Sens. 2021, 13(13), 2432;
Received: 26 May 2021 / Revised: 19 June 2021 / Accepted: 19 June 2021 / Published: 22 June 2021
Remote sensing images are widely used in object detection and tracking, military security, and other computer vision tasks. However, remote sensing images are often degraded by suspended aerosol in the air, especially under poor weather conditions, such as fog, haze, and mist. The quality of remote sensing images directly affect the normal operations of computer vision systems. As such, haze removal is a crucial and indispensable pre-processing step in remote sensing image processing. Additionally, most of the existing image dehazing methods are not applicable to all scenes, so the corresponding dehazed images may have varying degrees of color distortion. This paper proposes a novel atmospheric light estimation based dehazing algorithm to obtain high visual-quality remote sensing images. First, a differentiable function is used to train the parameters of a linear scene depth model for the scene depth map generation of remote sensing images. Second, the atmospheric light of each hazy remote sensing image is estimated by the corresponding scene depth map. Then, the corresponding transmission map is estimated on the basis of the estimated atmospheric light by a haze-lines model. Finally, according to the estimated atmospheric light and transmission map, an atmospheric scattering model is applied to remove haze from remote sensing images. The colors of the images dehazed by the proposed method are in line with the perception of human eyes in different scenes. A dataset with 100 remote sensing images from hazy scenes was built for testing. The performance of the proposed image dehazing method is confirmed by theoretical analysis and comparative experiments. View Full-Text
Keywords: haze removal; remote sensing image; atmospheric light; atmospheric scattering model haze removal; remote sensing image; atmospheric light; atmospheric scattering model
Show Figures

Graphical abstract

MDPI and ACS Style

Zhu, Z.; Luo, Y.; Wei, H.; Li, Y.; Qi, G.; Mazur, N.; Li, Y.; Li, P. Atmospheric Light Estimation Based Remote Sensing Image Dehazing. Remote Sens. 2021, 13, 2432.

AMA Style

Zhu Z, Luo Y, Wei H, Li Y, Qi G, Mazur N, Li Y, Li P. Atmospheric Light Estimation Based Remote Sensing Image Dehazing. Remote Sensing. 2021; 13(13):2432.

Chicago/Turabian Style

Zhu, Zhiqin, Yaqin Luo, Hongyan Wei, Yong Li, Guanqiu Qi, Neal Mazur, Yuanyuan Li, and Penglong Li. 2021. "Atmospheric Light Estimation Based Remote Sensing Image Dehazing" Remote Sensing 13, no. 13: 2432.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop