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Artificial Intelligence for Ocean Remote Sensing
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Artificial Intelligence for Ocean Remote Sensing

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 10709

Special Issue Editors

Prof. Dr. Xiao-Hai Yan

E-Mail Website
Guest Editor
Center for Remote Sensing, College of Earth, Ocean and Environment, University of Delaware, Newark, DE 19716, USA
Interests: physical oceanography; ocean remote sensing; climate change; air-sea interaction; ocean circulation; image processing; environmental monitoring; deep learning/big data/data science
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hua Su

E-Mail Website
Guest Editor
The Academy of Digital China, Fuzhou University, Fuzhou 350108, China
Interests: ocean remote sensing; coastal remote sensing; deep ocean remote sensing; global climate change; AI oceanography
Special Issues, Collections and Topics in MDPI journals
Dr. Wenfang Lu

E-Mail Website
Guest Editor
School of Marine Sciences, Sun Yat-Sen University, Guangzhou, China
Interests: physical oceanography; ocean remote sensing; AI oceanography; data science; bio-physical coupling

Special Issue Information

Dear Colleagues,

The use of Artificial Intelligence (AI) has the potential to revolutionize the way we collect, analyze, and interpret data from the vast and complex oceans. AI oceanography has demonstrated its capability in the handling of various oceanic problems, from monitoring marine ecosystems and the environment to predicting ocean currents and weather patterns. Concurrently, propelled by the continuous development of remote sensing techniques over recent decades, ocean observation has entered the big data era. An increasing number of ocean satellites equipped with broad sensors have been deployed to view oceans from large-scale and high-resolution perspectives.

The fusion of AI and remote sensing has unleased great potential in dealing with remote sensing retrieval, feature/pattern recognition, and reconstruction problems. The underlying rules of hidden correlation can be revealed from collected data to advance our understanding of the oceans and contribute to more effective protection and management efforts. By further combining these with other oceanic data, such as numerical models and reanalysis, the challenges faced by traditional oceanography can be effectively mitigated, and a new data-driven direction of ocean remote sensing can emerge as a new paradigm.

The main goal of this Special Issue is to provide a scientific platform to discuss recent advances in AI applications in the remote sensing of oceans. We welcome papers of both theoretical and applicative nature, as well as contributions regarding new advanced AI/machine learning, deep learning, and data science techniques for the remote sensing research community.

Prof. Dr. Xiao-Hai Yan
Prof. Dr. Hua Su
Dr. Wenfang Lu
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

  • ocean remote sensing
  • artificial intelligence
  • machine learning and deep learning
  • big data mining
  • data-driven model
  • ocean processes
  • ocean color and environment
  • coastal environment and disaster

Published Papers (6 papers)

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Research

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21 pages, 8221 KiB  
Article
Improving Short-Term Prediction of Ocean Fog Using Numerical Weather Forecasts and Geostationary Satellite-Derived Ocean Fog Data Based on AutoML
by Seongmun Sim, Jungho Im, Sihun Jung and Daehyeon Han
Remote Sens. 2024, 16(13), 2348; https://doi.org/10.3390/rs16132348 - 27 Jun 2024
Viewed by 410
Abstract
Ocean fog, a meteorological phenomenon characterized by reduced visibility due to tiny water droplets or ice particles, poses significant safety risks for maritime activities and coastal regions. Accurate prediction of ocean fog is crucial but challenging due to its complex formation mechanisms and [...] Read more.
Ocean fog, a meteorological phenomenon characterized by reduced visibility due to tiny water droplets or ice particles, poses significant safety risks for maritime activities and coastal regions. Accurate prediction of ocean fog is crucial but challenging due to its complex formation mechanisms and variability. This study proposes an advanced ocean fog prediction model for the Yellow Sea region, leveraging satellite-based detection and high-performance data-driven methods. We used Himawari-8 satellite data to obtain a lot of spatiotemporal ocean fog references and employed AutoML to integrate numerical weather prediction (NWP) outputs and sea surface temperature (SST)-related variables. The model demonstrated superior performance compared to traditional NWP-based methods, achieving high performance in both quantitative—probability of detection of 81.6%, false alarm ratio of 24.4%, f1 score of 75%, and proportion correct of 79.8%—and qualitative evaluations for 1 to 6 h lead times. Key contributing variables included relative humidity, accumulated shortwave radiation, and atmospheric pressure, indicating the importance of integrating diverse data sources. The study emphasizes the potential of using satellite-derived data to improve ocean fog prediction, while also addressing the challenges of overfitting and the need for more comprehensive reference data. Full article
(This article belongs to the Special Issue Artificial Intelligence for Ocean Remote Sensing)
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18 pages, 9615 KiB  
Article
Multi-Scale Window Spatiotemporal Attention Network for Subsurface Temperature Prediction and Reconstruction
by Jiawei Jiang, Jun Wang, Yiping Liu, Chao Huang, Qiufu Jiang, Liqiang Feng, Liying Wan and Xiangguang Zhang
Remote Sens. 2024, 16(12), 2243; https://doi.org/10.3390/rs16122243 - 20 Jun 2024
Viewed by 420
Abstract
In this study, we investigate the feasibility of using historical remote sensing data to predict the future three-dimensional subsurface ocean temperature structure. We also compare the performance differences between predictive models and real-time reconstruction models. Specifically, we propose a multi-scale residual spatiotemporal window [...] Read more.
In this study, we investigate the feasibility of using historical remote sensing data to predict the future three-dimensional subsurface ocean temperature structure. We also compare the performance differences between predictive models and real-time reconstruction models. Specifically, we propose a multi-scale residual spatiotemporal window ocean (MSWO) model based on a spatiotemporal attention mechanism, to predict changes in the subsurface ocean temperature structure over the next six months using satellite remote sensing data from the past 24 months. Our results indicate that predictions made using historical remote sensing data closely approximate those made using historical in situ data. This finding suggests that satellite remote sensing data can be used to predict future ocean structures without relying on valuable in situ measurements. Compared to future predictive models, real-time three-dimensional structure reconstruction models can learn more accurate inversion features from real-time satellite remote sensing data. This work provides a new perspective for the application of artificial intelligence in oceanography for ocean structure reconstruction. Full article
(This article belongs to the Special Issue Artificial Intelligence for Ocean Remote Sensing)
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20 pages, 26746 KiB  
Article
DAENet: Deformable Attention Edge Network for Automatic Coastline Extraction from Satellite Imagery
by Buyun Kang, Jian Wu, Jinyong Xu and Changshang Wu
Remote Sens. 2024, 16(12), 2076; https://doi.org/10.3390/rs16122076 - 7 Jun 2024
Viewed by 628
Abstract
Sea–land segmentation (SLS) is a crucial step in coastline extraction. In CNN-based approaches for coastline feature extraction, downsampling is commonly used to reduce computational demands. However, this method may unintentionally discard small-scale features, hindering the capture of essential global contextual information and clear [...] Read more.
Sea–land segmentation (SLS) is a crucial step in coastline extraction. In CNN-based approaches for coastline feature extraction, downsampling is commonly used to reduce computational demands. However, this method may unintentionally discard small-scale features, hindering the capture of essential global contextual information and clear edge information necessary for SLS. To solve this problem, we propose a novel U-Net structure called Deformable Attention Edge Network (DAENet), which integrates edge enhancement algorithms and a deformable self-attention mechanism. First of all, we designed a multi-scale transformation (MST) to enhance edge feature extraction and model convergence through multi-scale transformation and edge detection, enabling the network to capture spatial–spectral changes more effectively. This is crucial because the deformability of the Deformable Attention Transformer (DAT) modules increases training costs for model convergence. Moreover, we introduced DAT, which leverages its powerful global modeling capabilities and deformability to enhance the model’s recognition of irregular coastlines. Finally, we integrated the Local Adaptive Multi-Head Attention-based Edge Detection (LAMBA) module to enhance the spatial differentiation of edge features. We designed each module to address the complexity of SLS. Experiments on benchmark datasets demonstrate the superiority of the proposed DAENet over state-of-the-art methods. Additionally, we conducted ablation experiments to evaluate the effectiveness of each module. Full article
(This article belongs to the Special Issue Artificial Intelligence for Ocean Remote Sensing)
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17 pages, 3028 KiB  
Article
Evaluating Ecosystem Service Value Changes in Mangrove Forests in Guangxi, China, from 2016 to 2020
by Kedong Wang, Mingming Jia, Xiaohai Zhang, Chuanpeng Zhao, Rong Zhang and Zongming Wang
Remote Sens. 2024, 16(3), 494; https://doi.org/10.3390/rs16030494 - 27 Jan 2024
Viewed by 1102
Abstract
Mangrove forests play a vital role in maintaining ecological balance in coastal regions. Accurately assessing changes in the ecosystem service value (ESV) of these mangrove forests requires more precise distribution data and an appropriate set of evaluation methods. In this study, we accurately [...] Read more.
Mangrove forests play a vital role in maintaining ecological balance in coastal regions. Accurately assessing changes in the ecosystem service value (ESV) of these mangrove forests requires more precise distribution data and an appropriate set of evaluation methods. In this study, we accurately mapped the spatial distribution data and patterns of mangrove forests in Guangxi province in 2016 and 2020, using 10 m spatial resolution Sentinel-2 imagery, and conducted a comprehensive evaluation of ESV provided by mangrove forests. The results showed that (1) from 2016 to 2020, mangrove forests in Guangxi demonstrated a positive development trend and were undergoing a process of recovery. The area of mangrove forests in Guangxi increased from 6245.15 ha in 2016 to 6750.01 ha in 2020, with a net increase of 504.81 ha, which was mainly concentrated in Lianzhou Bay, Tieshan Harbour, and Dandou Bay; (2) the ESV of mangrove forests was USD 363.78 million in 2016 and USD 390.74 million in 2020; (3) the value of fishery, soil conservation, wave absorption, and pollution purification comprises the largest proportions of the ESV of mangrove forests. This study provides valuable insights and information to enhance our understanding of the relationship between the spatial pattern of mangrove forests and their ecosystem service value. Full article
(This article belongs to the Special Issue Artificial Intelligence for Ocean Remote Sensing)
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19 pages, 5115 KiB  
Article
Gas Plume Target Detection in Multibeam Water Column Image Using Deep Residual Aggregation Structure and Attention Mechanism
by Wenguang Chen, Xiao Wang, Binglong Yan, Junjie Chen, Tingchen Jiang and Jialong Sun
Remote Sens. 2023, 15(11), 2896; https://doi.org/10.3390/rs15112896 - 2 Jun 2023
Cited by 2 | Viewed by 1504
Abstract
A multibeam water column image (WCI) can provide detailed seabed information and is an important means of underwater target detection. However, gas plume targets in an image have no obvious contour information and are susceptible to the influence of underwater environments, equipment noises, [...] Read more.
A multibeam water column image (WCI) can provide detailed seabed information and is an important means of underwater target detection. However, gas plume targets in an image have no obvious contour information and are susceptible to the influence of underwater environments, equipment noises, and other factors, resulting in varied shapes and sizes. Compared with traditional detection methods, this paper proposes an improved YOLOv7 (You Only Look Once vision 7) network structure for detecting gas plume targets in a WCI. Firstly, Fused-MBConv is used to replace all convolutional blocks in the ELAN (Efficient Layer Aggregation Networks) module to form the ELAN-F (ELAN based on the Fused-MBConv block) module, which accelerates model convergence. Additionally, based on the ELAN-F module, MBConv is used to replace the 3 × 3 convolutional blocks to form the ELAN-M (ELAN based on the MBConv block) module, which reduces the number of model parameters. Both ELAN-F and ELAN-M modules are deep residual aggregation structures used to fuse multilevel features and enhance information expression. Furthermore, the ELAN-F1M3 (ELAN based on one Fused-MBConv block and three MBConv blocks) backbone network structure is designed to fully leverage the efficiency of the ELAN-F and ELAN-M modules. Finally, the SimAM attention block is added into the neck network to guide the network to pay more attention to the feature information related to the gas plume target at different scales and to improve model robustness. Experimental results show that this method can accurately detect gas plume targets in a complex WCI and has greatly improved performance compared to the baseline. Full article
(This article belongs to the Special Issue Artificial Intelligence for Ocean Remote Sensing)
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12 pages, 2509 KiB  
Technical Note
Enhancing Water Depth Estimation from Satellite Images Using Online Machine Learning: A Case Study Using Baidu Easy-DL with Acoustic Bathymetry and Sentinel-2 Data
by Zhongqiang Wu, Shulei Wu, Haixia Yang, Zhihua Mao and Wei Shen
Remote Sens. 2023, 15(20), 4955; https://doi.org/10.3390/rs15204955 - 13 Oct 2023
Cited by 2 | Viewed by 1700
Abstract
Water depth estimation is paramount in various domains, including navigation, environmental monitoring, and resource management. Traditional depth measurement methods, such as bathymetry, can often be expensive and time-consuming, especially in remote or inaccessible areas. This study delves into the application of machine learning [...] Read more.
Water depth estimation is paramount in various domains, including navigation, environmental monitoring, and resource management. Traditional depth measurement methods, such as bathymetry, can often be expensive and time-consuming, especially in remote or inaccessible areas. This study delves into the application of machine learning techniques, specifically focusing on the Baidu Easy DL model for water depth estimation leveraging satellite imagery. Utilizing Sentinel-2 satellite data over Rushikonda Beach in India and processing it into remote sensing reflectance using ACOLITE software, this research compares the performance of several machine learning algorithms, including the Stumpf model, Log-Linear model, and the Baidu Easy DL model, for accurate depth estimation. The results indicate that the Easy-DL model outperforms traditional methods, particularly excelling in the 0–11 m depth range. This study showcases the substantial potential of machine learning in remote sensing, offering robust water depth estimates, even in complex coastal environments. Furthermore, it underscores the critical role of comprehensive training datasets and ensemble learning techniques in enhancing accuracy. This research opens avenues for the further exploration of machine learning applications in remote sensing and highlights the promising prospects of online model APIs when streamlining remote sensing data processing. Full article
(This article belongs to the Special Issue Artificial Intelligence for Ocean Remote Sensing)
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