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Artificial Intelligence for Ocean 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: closed (30 November 2024) | Viewed by 23255

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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,

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

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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

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Published Papers (13 papers)

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20 pages, 8476 KiB

Open AccessArticle

AquaPile-YOLO: Pioneering Underwater Pile Foundation Detection with Forward-Looking Sonar Image Processing

by Zhongwei Xu, Rui Wang, Tianyu Cao, Wenbo Guo, Bo Shi and Qiqi Ge

Remote Sens. 2025, 17(3), 360; https://doi.org/10.3390/rs17030360 - 22 Jan 2025

Viewed by 316

Abstract

Underwater pile foundation detection is crucial for environmental monitoring and marine engineering. Traditional methods for detecting underwater pile foundations are labor-intensive and inefficient. Deep learning-based image processing has revolutionized detection, enabling identification through sonar imagery analysis. This study proposes an innovative methodology, named the AquaPile-YOLO algorithm, for underwater pile foundation detection. Our approach significantly enhances detection accuracy and robustness by integrating multi-scale feature fusion, improved attention mechanisms, and advanced data augmentation techniques. Trained on 4000 sonar images, the model excels in delineating pile structures and effectively identifying underwater targets. Experimental data show that the model can achieve good target identification results in similar experimental scenarios, with a 96.89% accuracy rate for underwater target recognition. Full article

(This article belongs to the Special Issue Artificial Intelligence for Ocean Remote Sensing)

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14 pages, 6956 KiB

Open AccessArticle

Enhanced Inversion of Sound Speed Profile Based on a Physics-Inspired Self-Organizing Map

by Guojun Xu, Ke Qu, Zhanglong Li, Zixuan Zhang, Pan Xu, Dongbao Gao and Xudong Dai

Remote Sens. 2025, 17(1), 132; https://doi.org/10.3390/rs17010132 - 2 Jan 2025

Viewed by 429

Abstract

The remote sensing-based inversion of sound speed profile (SSP) enables the acquisition of high-spatial-resolution SSP without in situ measurements. The spatial division of the inversion grid is crucial for the accuracy of results, determining both the number of samples and the consistency of inversion relationships. The result of our research is the introduction of a physics-inspired self-organizing map (PISOM) that facilitates SSP inversion by clustering samples according to the physical perturbation law. The linear physical relationship between sea surface parameters and the SSP drives dimensionality reduction for the SOM, resulting in the clustering of samples exhibiting similar disturbance laws. Subsequently, samples within each cluster are generalized to construct the topology of the solution space for SSP reconstruction. The PISOM method significantly improves accuracy compared with the SOM method without clustering. The PISOM has an SSP reconstruction error of less than 2 m/s in 25% of cases, while the SOM method has none. The transmission loss calculation also shows promising results, with an error of only 0.5 dB at 30 km, 5.5 dB smaller than that of the SOM method. A physical interpretation of the neural network processing confirms that physics-inspired clustering can bring better precision gains than the previous spatial grid. Full article

(This article belongs to the Special Issue Artificial Intelligence for Ocean Remote Sensing)

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20 pages, 10713 KiB

Open AccessArticle

Detecting Ocean Eddies with a Lightweight and Efficient Convolutional Network

by Haochen Sun, Hongping Li, Ming Xu, Tianyu Xia and Hao Yu

Remote Sens. 2024, 16(24), 4808; https://doi.org/10.3390/rs16244808 - 23 Dec 2024

Viewed by 516

Abstract

As a ubiquitous mesoscale phenomenon, ocean eddies significantly impact ocean energy and mass exchange. Detecting these eddies accurately and efficiently has become a research focus in ocean remote sensing. Many traditional detection methods, rooted in physical principles, often encounter challenges in practical applications due to their complex parameter settings, while effective, deep learning models can be limited by the high computational demands of their extensive parameters. Therefore, this paper proposes a new approach to eddy detection based on the altimeter data, the Ghost Attention Deeplab Network (GAD-Net), which is a lightweight and efficient semantic segmentation model designed to address these issues. The encoder of GAD-Net consists of a lightweight ECA+GhostNet and an Atrous Spatial Pyramid Pooling (ASPP) module. And the decoder integrates an Efficient Attention Network (EAN) module and an Efficient Ghost Feature Integration (EGFI) module. Experimental results show that GAD-Net outperforms other models in evaluation indices, with a lighter model size and lower computational complexity. It also outperforms other segmentation models in actual detection results in different sea areas. Furthermore, GAD-Net achieves detection results comparable to the Py-Eddy-Tracker (PET) method with a smaller eddy radius and a faster detection speed. The model and the constructed eddy dataset are publicly available. Full article

(This article belongs to the Special Issue Artificial Intelligence for Ocean Remote Sensing)

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23 pages, 7313 KiB

Open AccessArticle

Shallow Water Bathymetry Inversion Based on Machine Learning Using ICESat-2 and Sentinel-2 Data

by Mengying Ye, Changbao Yang, Xuqing Zhang, Sixu Li, Xiaoran Peng, Yuyang Li and Tianyi Chen

Remote Sens. 2024, 16(23), 4603; https://doi.org/10.3390/rs16234603 - 7 Dec 2024

Viewed by 1298

Abstract

Shallow water bathymetry is essential for maritime navigation, environmental monitoring, and coastal management. While traditional methods such as sonar and airborne LiDAR provide high accuracy, their high cost and time-consuming nature limit their application in remote and sensitive areas. Satellite remote sensing offers a cost-effective and rapid alternative for large-scale bathymetric inversion, but it still relies on significant in situ data to establish a mapping relationship between spectral data and water depth. The ICESat-2 satellite, with its photon-counting LiDAR, presents a promising solution for acquiring bathymetric data in shallow coastal regions. This study proposes a rapid bathymetric inversion method based on ICESat-2 and Sentinel-2 data, integrating spectral information, the Forel-Ule Index (FUI) for water color, and spatial location data (normalized X and Y coordinates and polar coordinates). An automated script for extracting bathymetric photons in shallow water regions is provided, aiming to facilitate the use of ICESat-2 data by researchers. Multiple machine learning models were applied to invert bathymetry in the Dongsha Islands, and their performance was compared. The results show that the XG-CID and RF-CID models achieved the highest inversion accuracies, 93% and 94%, respectively, with the XG-CID model performing best in the range from −10 m to 0 m and the RF-CID model excelling in the range from −15 m to −10 m. Full article

(This article belongs to the Special Issue Artificial Intelligence for Ocean Remote Sensing)

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21 pages, 9190 KiB

Open AccessArticle

Improving Atmospheric Correction Algorithms for Sea Surface Skin Temperature Retrievals from Moderate-Resolution Imaging Spectroradiometer Using Machine Learning Methods

by Bingkun Luo, Peter J. Minnett and Chong Jia

Remote Sens. 2024, 16(23), 4555; https://doi.org/10.3390/rs16234555 - 4 Dec 2024

Viewed by 628

Abstract

Satellite-retrieved sea-surface skin temperature (SST_skin) is essential for many Near-Real-Time studies. This study aimed to assess the potential to improve the accuracy of satellite-based SST_skin retrieval in the Caribbean region by using atmospheric correction algorithms based on four readily available machine learning (ML) approaches: eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Random Forest (RF), and the Artificial Neural Network (ANN). The ML models were trained on an extensive dataset comprising in situ SST measurements and atmospheric state parameters obtained from satellite products, reanalyzed datasets, research cruises, surface moorings, and drifting buoys. The benefits and shortcomings of various ML methods were assessed through comparisons with withheld in situ measurements. The results demonstrate that the ML-based algorithms achieve promising accuracy, with mean biases within 0.07 K when compared with the buoy data and ranging from −0.107 K to 0.179 K relative to the ship-derived SST_skin data. Notably, both XGBoost and RF stand out for their superior correlation and efficacy in the statistical results of validation. The improved SST_skin derived using the ML-based algorithms could enhance our understanding of vital oceanic and atmospheric characteristics and have the potential to reduce uncertainty in oceanographic, meteorological, and climate research. Full article

(This article belongs to the Special Issue Artificial Intelligence for Ocean Remote Sensing)

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23 pages, 8696 KiB

Open AccessArticle

Enhanced Fishing Monitoring in the Central-Eastern North Pacific Using Deep Learning with Nightly Remote Sensing

by Jiajun Li, Jinyou Li, Kui Zhang, Xi Li and Zuozhi Chen

Remote Sens. 2024, 16(22), 4312; https://doi.org/10.3390/rs16224312 - 19 Nov 2024

Viewed by 851

Abstract

The timely and accurate monitoring of high-seas fisheries is essential for effective management. However, efforts to monitor industry fishing vessels in the central-eastern North Pacific have been hampered by frequent cloud cover and solar illumination interference. In this study, enhanced fishing extraction algorithms based on computer vision were developed and tested. The results showed that YOLO-based computer vision models effectively detected dense small fishing targets, with original YOLOv8 achieving a precision (P) of 89% and a recall (R) of 79%, while refined versions improved these metrics to 93% and 99%, respectively. Compared with traditional threshold methods, the YOLO-based enhanced models showed significantly higher accuracy. While the threshold method could identify similar trend changes, it lacked precision in detecting individual targets, especially in blurry scenarios. Using our trained computer vision model, we established a dataset of dynamic changes in fishing vessels over the past decade. This research provides an accurate and reproducible process for precise monitoring of lit fisheries in the North Pacific, leveraging the operational and near-real-time capabilities of Google Earth Engine and computer vision. The approach can also be applied to dynamic monitoring of industrial lit fishing vessels in other regions. Full article

(This article belongs to the Special Issue Artificial Intelligence for Ocean Remote Sensing)

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25 pages, 4317 KiB

Open AccessArticle

Spatial Downscaling of Sea Surface Temperature Using Diffusion Model

by Shuo Wang, Xiaoyan Li, Xueming Zhu, Jiandong Li and Shaojing Guo

Remote Sens. 2024, 16(20), 3843; https://doi.org/10.3390/rs16203843 - 16 Oct 2024

Cited by 1 | Viewed by 1019

Abstract

In recent years, advancements in high-resolution digital twin platforms or artificial intelligence marine forecasting have led to the increased requirements of high-resolution oceanic data. However, existing sea surface temperature (SST) products from observations often fail to meet researchers’ resolution requirements. Deep learning models serve as practical techniques for improving the spatial resolution of SST data. In particular, diffusion models (DMs) have attracted widespread attention due to their ability to generate more vivid and realistic results than other neural networks. Despite DMs’ potential, their application in SST spatial downscaling remains largely unexplored. Hence we propose a novel DM-based spatial downscaling model, called DIFFDS, designed to obtain a high-resolution version of the input SST and to restore most of the meso scale processes. Experimental results indicate that DIFFDS is more effective and accurate than baseline neural networks, its downscaled high-resolution SST data are also visually comparable to the ground truth. The DIFFDS achieves an average root-mean-square error of 0.1074 °C and a peak signal-to-noise ratio of 50.48 dB in the 4× scale downscaling task, which shows its accuracy. Full article

(This article belongs to the Special Issue Artificial Intelligence for Ocean Remote Sensing)

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21 pages, 8221 KiB

Open AccessArticle

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

Cited by 1 | Viewed by 1383

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 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

Open AccessArticle

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

Cited by 2 | Viewed by 1169

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 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

Open AccessArticle

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

Cited by 1 | Viewed by 1475

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 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

Open AccessArticle

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

Cited by 4 | Viewed by 1772

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 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

Open AccessArticle

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 5 | Viewed by 2047

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 Artificial Intelligence for Ocean 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

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 3 | Viewed by 2751

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 Artificial Intelligence for Ocean Remote Sensing)

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