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Special Issue "Ocean Monitoring Based on Artificial Intelligence and Remote Sensing"

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

Deadline for manuscript submissions: 26 April 2024 | Viewed by 3655

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Special Issue Editors

Prof. Dr. Xiao-Hai Yan

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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

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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,

Ocean environmental monitoring plays a crutial role in understanding and utilization of the ocean, involving many fields such as ocean’s role in climate change, ocean energy development and protection, ocean transportation, fisheries, and ocean disaster prevention, etc. Due to the dynamic nature of the ocean and sparcity of in-situ ocean observation, monitoring the ocean from space has been a difficult task. With the continuous development of remote sensing and artificial intelligence technologies during recent years, ocean monitoring has entered the big-data era. More and more ocean satellites equipped with broad sensors have been deployed to view the ocean from a large scale and high-resolution perspective.

Moreover, the combination of the two technologies has unleased great potential in dealing with complex remote sensing retrival, feature/pattern recognition, and reconstruction problems. By combining remote sensing technology, existing rules of value and hidden correlation can be discovered from the data, to better observe the ocean and coastal environment. This can effectively avoid the defects faced by traditional ocean monitoring and provide a new data-driven direction for the development of AI-based ocean monitoring.

The main goal of this Special Issue is to provide a scientific platform to discuss recent advances in the application of remote sensing and AI techniques to monitor the ocean and coastal environment. 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, are welcome.

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 monitoring
remote sensing
artificial intelligence
machine learning and deep learning
big data
data-driven model
ocean processes
sea level, altimetry, ocean color and environment
coastal environment and disaster

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19 pages, 5115 KiB

Open AccessArticle

Gas Plume Target Detection in Multibeam Water Column Image Using Deep Residual Aggregation Structure and Attention Mechanism

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Remote Sens. 2023, 15(11), 2896; https://doi.org/10.3390/rs15112896 - 02 Jun 2023

Cited by 1 | Viewed by 964

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, 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 Ocean Monitoring Based on Artificial Intelligence and Remote Sensing)

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12 pages, 2509 KiB

Open AccessTechnical 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

and

Remote Sens. 2023, 15(20), 4955; https://doi.org/10.3390/rs15204955 - 13 Oct 2023

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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 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 Ocean Monitoring Based on Artificial Intelligence and Remote Sensing)

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