Atmospheric scattering model and dark channel prior constraint network for environmental monitoring under hazy conditions

IF 5.9 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES Journal of Environmental Sciences-china Pub Date : 2024-04-29 DOI:10.1016/j.jes.2024.04.037
Lintao Han , Hengyi Lv , Chengshan Han , Yuchen Zhao , Qing Han , Hailong Liu
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Abstract

Environmental monitoring systems based on remote sensing technology have a wider monitoring range and longer timeliness, which makes them widely used in the detection and management of pollution sources. However, haze weather conditions degrade image quality and reduce the precision of environmental monitoring systems. To address this problem, this research proposes a remote sensing image dehazing method based on the atmospheric scattering model and a dark channel prior constrained network. The method consists of a dehazing network, a dark channel information injection network (DCIIN), and a transmission map network. Within the dehazing network, the branch fusion module optimizes feature weights to enhance the dehazing effect. By leveraging dark channel information, the DCIIN enables high-quality estimation of the atmospheric veil. To ensure the output of the deep learning model aligns with physical laws, we reconstruct the haze image using the prediction results from the three networks. Subsequently, we apply the traditional loss function and dark channel loss function between the reconstructed haze image and the original haze image. This approach enhances interpretability and reliability while maintaining adherence to physical principles. Furthermore, the network is trained on a synthesized non-homogeneous haze remote sensing dataset using dark channel information from cloud maps. The experimental results show that the proposed network can achieve better image dehazing on both synthetic and real remote sensing images with non-homogeneous haze distribution. This research provides a new idea for solving the problem of decreased accuracy of environmental monitoring systems under haze weather conditions and has strong practicability.

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用于灰霾条件下环境监测的大气散射模型和暗通道先验约束网络
基于遥感技术的环境监测系统具有更广的监测范围和更长的时效性,因此被广泛应用于污染源的检测和管理。然而,雾霾天气会降低图像质量,降低环境监测系统的精度。针对这一问题,本研究提出了一种基于大气散射模型和暗通道先验约束网络的遥感图像去雾方法。该方法由去毛刺网络、暗信道信息注入网络(DCIIN)和传输图网络组成。在除杂网络中,分支融合模块优化特征权重,以增强除杂效果。通过利用暗信道信息,DCIIN 可以高质量地估计大气面纱。为确保深度学习模型的输出符合物理规律,我们利用三个网络的预测结果重建雾霾图像。随后,我们在重建的雾霾图像和原始雾霾图像之间应用传统损失函数和暗通道损失函数。这种方法既提高了可解释性和可靠性,又符合物理原理。此外,我们还利用云图中的暗信道信息,在合成的非均质雾霾遥感数据集上对网络进行了训练。实验结果表明,所提出的网络能在非均质雾度分布的合成和真实遥感图像上实现更好的图像去毛刺效果。该研究为解决雾霾天气条件下环境监测系统精度下降的问题提供了新思路,具有很强的实用性。
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来源期刊
Journal of Environmental Sciences-china
Journal of Environmental Sciences-china 环境科学-环境科学
CiteScore
13.70
自引率
0.00%
发文量
6354
审稿时长
2.6 months
期刊介绍: The Journal of Environmental Sciences is an international journal started in 1989. The journal is devoted to publish original, peer-reviewed research papers on main aspects of environmental sciences, such as environmental chemistry, environmental biology, ecology, geosciences and environmental physics. Appropriate subjects include basic and applied research on atmospheric, terrestrial and aquatic environments, pollution control and abatement technology, conservation of natural resources, environmental health and toxicology. Announcements of international environmental science meetings and other recent information are also included.
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