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Remote Sensing
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Artificial Intelligence and Big Data for Oceanography (2nd Edition)
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Artificial Intelligence and Big Data for Oceanography (2nd Edition)

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1531

Special Issue Editors

Dr. Yongqing Li

E-Mail Website
Guest Editor
School of Data Science, Qingdao University of Science and Technology, Qingdao 266061, China
Interests: remote sensing; oceanic disaster prediction; deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Weimin Huang

E-Mail Website
Guest Editor
Faculty of Engineering and Applied Science, Memorial University, St. John’s, NL A1B 3X5, Canada
Interests: mapping of oceanic surface parameters via high-frequency ground wave radar; X-band marine radar; global navigation satellite systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Peng Ren

E-Mail Website
Guest Editor
College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China
Interests: remote sensing; oceanic engineering; machine learning; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Oceanography refers to the scientific study of the oceans. It involves multiple disciplines such as astronomy, biology, chemistry, climatology, geography, geology, hydrology, meteorology and physics. In recent decades, with the development of remote sensing and other observation technology, oceanography has been extensively enriched by the amount and variety of observation data. The big data enable state-of-the-art artificial intelligence methods to further increase the depth and width of oceanography. The artificial intelligence methods can effectively mine useful ocean information from a large amount of oceanographic observation data. Recent studies have shown the advantages of artificial intelligence methods in terms of processing oceanographic data. Therefore, oceanography incorporating artificial intelligence and big data is an important research topic.

This Special Issue aims to gather studies about artificial intelligence and big data-based oceanography. The studies may cover the acquisition of big observation data, design of artificial intelligence models, analysis of specific oceanographic issues, and other related topics. Oceanographic data are mainly obtained using remote sensing technology. The journal encourages artificial intelligence methods for processing remote sensing data. Hence, the subject is closely related to the journal scope.

Articles may address, but are not limited to, the following topics related to artificial intelligence and big data:

  • Oceanographic data acquisition;
  • Meteorological forecast;
  • Oceanic disaster prediction;
  • Climate anomaly warning;
  • Multisource meteorological observation;
  • Oceanic information extraction;
  • Oil spill trajectory prediction;
  • Sea ice detection and prediction;
  • Algal bloom detection and prediction;
  • Mesoscale eddy detection;
  • Internal ocean wave detection;
  • Coastal remote sensing.

Dr. Yongqing Li
Prof. Dr. Weimin Huang
Prof. Dr. Peng Ren
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

  • artificial intelligence
  • oceanography
  • big data
  • remote sensing
  • information extraction
  • data mining

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Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (2 papers)

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Research

21 pages, 7916 KiB  
Article
A Novel Sea Surface Temperature Prediction Model Using DBN-SVR and Spatiotemporal Secondary Calibration
by Yibo Liu, Zichen Zhao, Zhe Zhang and Yi Yang
Remote Sens. 2025, 17(10), 1681; https://doi.org/10.3390/rs17101681 - 10 May 2025
Viewed by 247
Abstract
Sea surface temperature (SST) is crucial for weather forecasting, climate modeling, and environmental monitoring. This study proposes a novel prediction model that achieves a 60-day forecast with a root mean square error (RMSE) consistently below 0.9 °C. The model combines the nonlinear feature [...] Read more.
Sea surface temperature (SST) is crucial for weather forecasting, climate modeling, and environmental monitoring. This study proposes a novel prediction model that achieves a 60-day forecast with a root mean square error (RMSE) consistently below 0.9 °C. The model combines the nonlinear feature extraction of a deep belief network (DBN) with the high-precision regression of support vector regression (SVR), enhanced by spatiotemporal secondary calibration (SSC) to better capture SST variation patterns. Using satellite-derived remote sensing data, the DBN-SVR model outperforms baseline methods in both the Indian Ocean and North Pacific regions, demonstrating strong applicability across diverse marine environments. This work advances long-term SST prediction capabilities, providing a reliable foundation for extended-range marine forecasts. Full article
(This article belongs to the Special Issue Artificial Intelligence and Big Data for Oceanography (2nd Edition))
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24 pages, 11846 KiB  
Article
DVR: Towards Accurate Hyperspectral Image Classifier via Discrete Vector Representation
by Jiangyun Li, Hao Wang, Xiaochen Zhang, Jing Wang, Tianxiang Zhang and Peixian Zhuang
Remote Sens. 2025, 17(3), 351; https://doi.org/10.3390/rs17030351 - 21 Jan 2025
Cited by 1 | Viewed by 772
Abstract
In recent years, convolutional neural network (CNN)-based and transformer-based approaches have made strides in improving the performance of hyperspectral image (HSI) classification tasks. However, misclassifications are unavoidable in the aforementioned methods, with a considerable number of these issues stemming from the overlapping embedding [...] Read more.
In recent years, convolutional neural network (CNN)-based and transformer-based approaches have made strides in improving the performance of hyperspectral image (HSI) classification tasks. However, misclassifications are unavoidable in the aforementioned methods, with a considerable number of these issues stemming from the overlapping embedding spaces among different classes. This overlap results in samples being allocated to adjacent categories, thus leading to inaccurate classifications. To mitigate these misclassification issues, we propose a novel discrete vector representation (DVR) strategy for enhancing the performance of HSI classifiers. DVR establishes a discrete vector quantification mechanism to capture and store distinct category representations in the codebook between the encoder and classification head. Specifically, DVR comprises three components: the Adaptive Module (AM), Discrete Vector Constraints Module (DVCM), and auxiliary classifier (AC). The AM aligns features derived from the backbone to the embedding space of the codebook. The DVCM employs category representations from the codebook to constrain encoded features for a rational feature distribution of distinct categories. To further enhance accuracy, the AC correlates discrete vectors with category information obtained from labels by penalizing these vectors and propagating gradients to the encoder. It is worth noting that DVR can be seamlessly integrated into HSI classifiers with diverse architectures to enhance their performance. Numerous experiments on four HSI benchmarks demonstrate that our DVR scheme improves the classifiers’ performance in terms of both quantitative metrics and visual quality of classification maps. We believe DVR can be applied to more models in the future to enhance their performance and provide inspiration for tasks such as sea ice detection and algal bloom prediction in the marine domain. Full article
(This article belongs to the Special Issue Artificial Intelligence and Big Data for Oceanography (2nd Edition))
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