Research on infrared hyperspectral remote sensing cloud detection method based on deep learning

高光谱成像 遥感 计算机科学 云计算 卷积神经网络 深度学习 人工智能 红外线的 环境科学 地质学 物理 光学 操作系统
作者
Zhuoya Ni,Mengdie Wu,Qifeng Lu,Hongyuan Huo,Fu Wang
出处
期刊:International Journal of Remote Sensing [Taylor & Francis]
卷期号:: 1-21 被引量:1
标识
DOI:10.1080/01431161.2023.2221806
摘要

Infrared hyperspectral is susceptible to clouds, and accurately identifying whether the hyperspectral infrared sounder data is polluted by clouds is of great significance for numerical weather prediction and atmospheric parameter inversion. Since the complex spectral characteristics of clouds, the existing spectral threshold methods and machine learning methods have the difficulties of undetermined threshold and clear field of view (FOV) missed and false detections. In order to improve the cloud recognition accuracy of infrared hyperspectral data, three end-to-end cloud detection models combining deep neural network (DNN) and convolutional neural network (CNN) and long short-term memory network (LSTM) are proposed. In this paper, taking the High Spectral Infrared Atmospheric Sounder (HIRAS) equipped with Fengyun-3D (FY-3D) satellite as the research object, based on the same platform Moderate Resolution Spectral Imager-II (MERSI-II) cloud mask (CLM) product, the HIRAS Cloud dataset is established, and the accuracy test and qualitative analysis are carried out by using the test datasets and Typhoon Siamba, July 3, 2022, as well as the earth observation scene under the conditions of ice and snow surface. The test datasets analysis results show that the cloud detection accuracy of CNN and CNN-LSTM model is stable at 0.96, and the false alarm rate of cloud is 0.035 and 0.036, respectively, and the detection ability of DNN model is slightly inferior to the former two in the same hidden layer, with an accuracyof 0.94. In further qualitative research, we found that the CNN-LSTM model has high accuracy and robustness in infrared hyperspectral cloud detection, and the detection results in a variety of surface scenarios are consistent with the actual situation of whether clouds occur in the FOV of the instrument. Compared with CLM products, it can better identify clear ocean scenes, and provide fast and efficient cloud detection reference for data assimilation systems.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
2秒前
小李博士发布了新的文献求助10
3秒前
感动鞋垫发布了新的文献求助10
4秒前
菲菲发布了新的文献求助10
4秒前
paul发布了新的文献求助10
6秒前
6秒前
6秒前
任性的数据线完成签到,获得积分10
8秒前
JamesPei应助小李博士采纳,获得10
9秒前
完美世界应助超人也读博采纳,获得10
10秒前
10秒前
费笑柳发布了新的文献求助10
10秒前
11秒前
11秒前
paul完成签到,获得积分10
12秒前
13秒前
ding应助奔波儿灞采纳,获得10
14秒前
pangao完成签到,获得积分10
14秒前
14秒前
Prime完成签到,获得积分10
14秒前
wanci应助科研通管家采纳,获得10
16秒前
Lucas应助科研通管家采纳,获得10
16秒前
FashionBoy应助科研通管家采纳,获得30
16秒前
乐观小之应助科研通管家采纳,获得10
16秒前
16秒前
英俊的铭应助科研通管家采纳,获得10
16秒前
我是老大应助科研通管家采纳,获得10
16秒前
斯文败类应助紫心采纳,获得10
16秒前
彭于晏应助科研通管家采纳,获得10
16秒前
我是老大应助科研通管家采纳,获得10
16秒前
科目三应助科研通管家采纳,获得10
17秒前
Prime发布了新的文献求助10
17秒前
思源应助科研通管家采纳,获得10
17秒前
17秒前
CodeCraft应助科研通管家采纳,获得10
17秒前
顾矜应助科研通管家采纳,获得10
17秒前
酷波er应助科研通管家采纳,获得10
17秒前
Niuzaihenmang发布了新的文献求助10
17秒前
17秒前
17秒前
高分求助中
Ophthalmic Equipment Market by Devices(surgical: vitreorentinal,IOLs,OVDs,contact lens,RGP lens,backflush,diagnostic&monitoring:OCT,actorefractor,keratometer,tonometer,ophthalmoscpe,OVD), End User,Buying Criteria-Global Forecast to2029 2000
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3962851
求助须知:如何正确求助?哪些是违规求助? 3508777
关于积分的说明 11143063
捐赠科研通 3241643
什么是DOI,文献DOI怎么找? 1791638
邀请新用户注册赠送积分活动 873002
科研通“疑难数据库(出版商)”最低求助积分说明 803577