一种基于微分增强的云检测改进算法

doi:10.3969/j.issn.1673-6141.2024.04.002

大气与环境光学学报 ›› 2024, Vol. 19 ›› Issue (4): 405-417.doi: 10.3969/j.issn.1673-6141.2024.04.002

一种基于微分增强的云检测改进算法

疏旭 1,2, 王珍珠 2,3,4*, 邓淑梅 1, 况志强 2,4, 吴德成 2,3, 刘东 2,3,4

1 安徽建筑大学环境与能源工程学院, 安徽合肥 230601; 2 中国科学院合肥物质科学研究院安徽光学精密机械研究所, 中国科学院大气光学重点实验室, 安徽合肥 230031; 3 先进激光技术安徽省实验室, 安徽合肥 230037; 4 中国科学技术大学, 安徽合肥 230026

收稿日期:2022-05-10 修回日期:2022-06-12 出版日期:2024-07-28 发布日期:2024-07-30
通讯作者: E-mail: zzwang@aiofm.ac.cn E-mail:zzwang@aiofm.ac.cn
作者简介:疏旭 (1997- ), 安徽铜陵人, 硕士研究生, 主要从事激光雷达大气探测技术与应用方面的研究。E-mail: shuxu17718150732@163.com
基金资助:
国家自然科学基金项目 (41975038, 42111530028), 安徽省重点研究与开发计划 (202004b11020012), 安徽省自然科学基金杰青项目 (2008085J33), 中国科学院青年创新促进会人才项目 (Y2021113)

An improved cloud detection algorithm based on differential enhancement

SHU Xu 1,2, WANG Zhenzhu 2,3,4*, DENG Shumei 1, KUANG Zhiqiang 2,4, WU Decheng 2,3, LIU Dong 2,3,4

1 School of Environment and Energy Engineering, Anhui Jianzhu University, Hefei 230601, China; 2 Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, HFIPS, Chinese Academy of Sciences, Hefei 230031, China; 3 Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, China; 4 University of Science and Technology of China, Hefei 230026, China

Received:2022-05-10 Revised:2022-06-12 Online:2024-07-28 Published:2024-07-30

摘要/Abstract

摘要： 激光雷达是大气主动遥感探测的强有力工具之一，可用于对大气中云的时空分布进行自动连续监测。提出一种基于微分增强的云检测改进算法，该方法通过引入积分面积和峰值比算法，不仅能有效减少微分时数据拟合的点数，提高云层数据的真实性，还能避免微分后强噪声点带来的误判；此外，改进算法还添加了特征云层的合并，可有效减少原算法在单层多峰信号情况下带来的漏判。最后，利用模拟的三种激光雷达信号和实测的米散射激光雷达信号进行算法改进前后的分析对比。结果表明，改进后算法保持了在低信噪比条件下检测云层的优越性，同时进一步减少特征云层的误判和漏判。

关键词: 激光雷达, 云检测, 微分增强, 信号模拟

Abstract: As a powerful tool for active remote sensing of the atmosphere， lidar can be used to monitor the spatial and temporal distribution of clouds automatically and continuously. An improved cloud detection algorithm based on differential enhancement is proposed in this work. By introducing integral area and peak ratio algorithm， the improved algorithm cannot only effectively reduce the number of data fitting points during differentiation， improve the authenticity of cloud data， but also avoid misjudgment caused by strong noise points after differentiation in the traditional differential enhancing cloud detection algorithm. In addition， the improved algorithm also adds the merging of characteristic clouds， which can effectively reduce the mission caused by the traditional algorithm in the case of single-layer and multi-peak signals. Finally， the three kinds of simulated lidar signals and the measured Mie scattered lidar signals are used to analyze and compare the algorithms before and after the improvement. The results show that the improved algorithm maintains the superiority of cloud detection under the conditions of low signal-to-noise ratio， while further reduces the misjudgment and mission of characteristic clouds.

Key words: lidar, cloud detection, differential enhancement, signal simulation

中图分类号:

P412.1

疏旭, 王珍珠, 邓淑梅, 况志强, 吴德成, 刘东, . 一种基于微分增强的云检测改进算法[J]. 大气与环境光学学报, 2024, 19(4): 405-417.

SHU Xu , WANG Zhenzhu , DENG Shumei , KUANG Zhiqiang , WU Decheng , LIU Dong , . An improved cloud detection algorithm based on differential enhancement[J]. Journal of Atmospheric and Environmental Optics, 2024, 19(4): 405-417.

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一种基于微分增强的云检测改进算法

An improved cloud detection algorithm based on differential enhancement

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