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Analysis of Satellite Cloud Images via Deep Learning Techniques

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 3683

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Special Issue Editors

Dr. Pak-Wai Chan

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Aviation Weather Services, Hong Kong Observatory, Hong Kong 999077, China
Interests: aviation meteorology; aviation safety; mountain meteorology; meteorological instrumentation
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Prof. Dr. Yun-Cheng He

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Research Center for Wind Engineering and Engineering Vibration, Guangzhou University, Guangzhou 510006, China
Interests: atmospheric observations; wind engineering; structural health monitoring; computational fluid mechanics; structural engineering
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Dr. Yang-Tao Wang

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School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China
Interests: deep learning; image recognition; graph neural network and multimedia content analysis

Dr. Teng Wu

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Department of Civil, Structural and Environmental Engineering, University at Buffalo, Buffalo, NY 14260, USA
Interests: wind engineering; bridge engineering; structural engineering; hurricane resilience; machine learning; climate change
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Dr. Ismail Gultepe

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Engineering and Applied Science Department, Ontario Technical University, Oshawa, ON L1G 0C5, Canada
Interests: atmospheric physics; climate change; remote sensing
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Special Issue Information

Dear Colleagues,

With the improvements made in satellite remote sensing technology and imaging technology, the spatial resolution and timeliness of satellite cloud image data have been dramatically improved. These data provide potent means for monitoring disastrous weather, such as typhoons and rainstorms, and play a vital role in weather forecasts and short-term climate prediction. However, owing to the increasing complexity of satellite cloud images in both temporal and spatial dimensions, traditional methods cannot effectively recognize and analyze these data. Over the past few years, deep learning techniques, such as convolution neural network, recurrent neural network and recent vision transformer, have achieved great success in various computer vision applications by automatically capturing and learning the key features of image data. Their powerful feature extraction abilities show great potential for analyzing complex spatio-temporal data like satellite cloud images.

This Special Issue invites scholars to submit manuscripts that present new deep learning models or introduce the most advanced deep learning techniques for processing and analyzing satellite cloud images. As this is a broad area, there are no constraints regarding the field of applications. Potential topics include, but discussions are not limited to, the following areas:

Satellite cloud image classification;
Satellite cloud image restoration;
Satellite cloud image prediction;
Object detection of satellite cloud image;
Spatio-temporal analysis of satellite cloud image;
Applications to satellite cloud image

Dr. Pak-Wai Chan
Dr. Yun-Cheng He
Dr. Yang-Tao Wang
Dr. Teng Wu
Dr. Ismail Gultepe
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

satellite cloud image
deep learning
object detection
classification
prediction
restoration
tropical cyclone

Published Papers (3 papers)

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Research

25 pages, 9091 KiB

Open AccessArticle

A Machine-Learning-Based Study on All-Day Cloud Classification Using Himawari-8 Infrared Data

by Yashuai Fu, Xiaofei Mi, Zhihua Han, Wenhao Zhang, Qiyue Liu, Xingfa Gu and Tao Yu

Remote Sens. 2023, 15(24), 5630; https://doi.org/10.3390/rs15245630 - 05 Dec 2023

Viewed by 1102

Abstract

Clouds are diverse and complex, making accurate cloud type identification vital in improving the accuracy of weather forecasting and the effectiveness of climate monitoring. However, current cloud classification research has largely focused on daytime data. The lack of visible light data at night presents challenges in characterizing nocturnal cloud attributes, leading to difficulties in achieving continuous all-day cloud classification results. This study proposed an all-day infrared cloud classification model (AInfraredCCM) based on XGBoost. Initially, the latitude/longitude, 10 infrared channels, and 5 brightness temperature differences of the Himawari-8 satellite were selected as input features. Then, 1,314,275 samples were collected from the Himawari-8 full-disk data and cloud classification was conducted using the CPR/CALIOP merged cloud type product as training data. The key cloud types included cirrus, deep convective, altostratus, altocumulus, nimbostratus, stratocumulus, stratus, and cumulus. The cloud classification model achieved an overall accuracy of 86.22%, along with precision, recall, and F1-score values of 0.88, 0.84, and 0.86, respectively. The practicality of this model was validated across all-day temporal, daytime/nighttime, and seasonal scenarios. The results showed that the AInfraredCCM consistently performed well across various time periods and seasons, confirming its temporal applicability. In conclusion, this study presents an all-day cloud classification approach to obtain comprehensive cloud information for continuous weather monitoring, ultimately enhancing weather prediction accuracy and climate monitoring. Full article

(This article belongs to the Special Issue Analysis of Satellite Cloud Images via Deep Learning Techniques)

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18 pages, 13574 KiB

Open AccessArticle

Recognition of Severe Convective Cloud Based on the Cloud Image Prediction Sequence from FY-4A

by Qi Chen, Xiaobin Yin, Yan Li, Peinan Zheng, Miao Chen and Qing Xu

Remote Sens. 2023, 15(18), 4612; https://doi.org/10.3390/rs15184612 - 20 Sep 2023

Viewed by 836

Abstract

Severe convective weather is hugely destructive, causing significant loss of life and social and economic infrastructure. Based on the U-Net network with the attention mechanism, the recurrent convolution, and the residual module, a new model is proposed named ARRU-Net (Attention Recurrent Residual U-Net) for the recognition of severe convective clouds using the cloud image prediction sequence from FY-4A data. The characteristic parameters used to recognize severe convective clouds in this study were brightness temperature values TBB₉, brightness temperature difference values TBB₉−TBB₁₂ and TBB₁₂−TBB₁₃, and texture features based on spectral characteristics. This method first input five satellite cloud images with a time interval of 30 min into the ARRU-Net model and predicted five satellite cloud images for the next 2.5 h. Then, severe convective clouds were segmented based on the predicted image sequence. The root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and correlation coefficient (R²) of the predicted results were 5.48 K, 35.52 dB, and 0.92, respectively. The results of the experiments showed that the average recognition accuracy and recall of the ARRU-Net model in the next five moments on the test set were 97.62% and 83.34%, respectively. Full article

(This article belongs to the Special Issue Analysis of Satellite Cloud Images via Deep Learning Techniques)

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26 pages, 11383 KiB

Open AccessArticle

Estimation of Tropical Cyclone Intensity via Deep Learning Techniques from Satellite Cloud Images

by Biao Tong, Jiyang Fu, Yaxue Deng, Yongjun Huang, Pakwai Chan and Yuncheng He

Remote Sens. 2023, 15(17), 4188; https://doi.org/10.3390/rs15174188 - 25 Aug 2023

Cited by 2 | Viewed by 1224

Abstract

Estimating the intensity of tropical cyclones (TCs) is usually involved as a critical step in studies on TC disaster warnings and prediction. Satellite cloud images (SCIs) are one of the most effective and preferable data sources for TC research. Despite the great achievements in various SCI-based studies, accurate and efficient estimation of TC intensity still remains a challenge. In recent years, machine learning (ML) techniques have gained fast development and shown significant potential in dealing with big data, particularly with images. This study focuses on the objective estimation of TC intensity based on SCIs via a comprehensive usage of some advanced deep learning (DL) techniques and smoothing methods. Two estimation strategies are proposed and examined which, respectively, involve one and two functional stages. The one-stage strategy uses Vision Transformer (ViT) or Deep Convolutional Neutral Network (DCNN) as the regression model for directly identifying TC intensity, while the second strategy involves a classification stage that aims to stratify SCI samples into a few intensity groups and a subsequent regression stage that specifies the TC intensity. Further efforts are made to improve the estimation accuracy by using smoothing manipulations (via four specific smoothing techniques) in the scenarios of the aforementioned two strategies and their fusion. Results show that DCNN performs better than ViT in the one-stage strategy, while using ViT as the classification model and DCNN as the regression model can result in the best performance in the two-stage strategy. It is interesting that although the strategy of singly using DCNN wins out over any concerned two-stage strategy, the fusion of the two strategies outperforms either the one-stage strategy or the two-stage strategy. Results also suggest that using smoothing techniques are beneficial for the improvement of estimation accuracy. Overall, the best performance is achieved by using a hybrid strategy that consists of the one-stage strategy, the two-stage strategy and smoothing manipulation. The associated RMSE and MAE values are 9.81 kt and 7.51 kt, which prevail over those from most existing studies. Full article

(This article belongs to the Special Issue Analysis of Satellite Cloud Images via Deep Learning Techniques)

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