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Special Issue "AI-based Remote Sensing Oceanography"

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

Deadline for manuscript submissions: 31 July 2019

Special Issue Editors

Guest Editor
Dr. Xiaofeng Li

NCWCP - E/RA3, 5830 University Research Court, College Park, MD 20740, U.S.A.
Website | E-Mail
Phone: (301)683-3314
Fax: (301)683-3301
Interests: AI oceanography, big data, ocean remote sensing, physical oceanography, boundary layer meteorology, synthetic aperture radar imaging mechanism, multiple-polarization radar applications, satellite image classification and segmentation
Guest Editor
Dr. Gang Zheng

State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, No.36 Baochubei Road, Xihu District, Hangzhou 310012, China
Website | E-Mail
Interests: AI oceanography, satellite oceanography, microwave remote sensing, image processing, tropical cyclone remote sensing
Guest Editor
Dr. Bin Liu

Shanghai Ocean University, 999 Hucheng Ring Rd, Pudong Xinqu, Shanghai 201306, China
Website | E-Mail
Interests: AI oceanography, data science and machine learning, SAR image processing and information retrieval, target detection and classification, image segmentation and classification

Special Issue Information

Dear Colleagues,

Ocean remote sensing is a research area that has undergone tremendous development in the past few decades. Much research has led to the operational implementation of many scientific algorithms to generate geoscience products that support the general public. The rapid development of satellites and sensors has caused a dramatic increase in both the amount and diversity of ocean remote sensing data, and such data requires extensive analysis and powerful technology to be understood. In the past few years, artificial intelligent (AI) technology has been widely used in many research fields for big data information mining and shown great potential in computer vision, natural language processing, and bioinformatics, among others. The number of AI-related papers has increased exponentially.

In order to consolidate the papers within the scope of AI applications in oceanography, this Special Issue on “AI-Based Remote Sensing Oceanography” will focus on the following four major disciplines:

  1. AI-based remote sensing image CLASSIFCATION (oil, ship, coastal zone, inundation, harmful algal bloom, cloud, eddy, internal wave, etc.)
  2. AI-based remote sensing data FUSION (altimeter, scatterometer, radiometer, hyperspectral, aerial photos, etc.)
  3. AI-based remote sensing ALGORITHM development (sea surface temperature, ocean color, wind, wave, dynamic topography, sea ice, etc.)
  4. AI-based ocean and marine meteorology FORECAST (sea surface temperature, storm, etc.)

We would like to invite articles on ocean-related studies using state-of-the-art AI techniques.

All submitted manuscripts will go through a peer review process. We look forward to receiving your submissions in this booming area of research.

Dr. Xiaofeng Li
Dr. Gang Zheng
Dr. Bin Liu
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 papers will be 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 1800 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

  • AI
  • Remote sensing
  • Oceanography
  • Coastal zone
  • Marine meteorology

Published Papers (4 papers)

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Research

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Open AccessArticle
Assimilation of SMOS Sea Surface Salinity in the Regional Ocean Model for South China Sea
Remote Sens. 2019, 11(8), 919; https://doi.org/10.3390/rs11080919
Received: 25 February 2019 / Revised: 27 March 2019 / Accepted: 12 April 2019 / Published: 16 April 2019
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Abstract
Ocean salinity has an important impact on marine environment simulations. The Soil Moisture and Ocean Salinity (SMOS) mission is the first satellite in the world to provide large-scale global salinity observations of the oceans. Salinity remote sensing observations in the open ocean have [...] Read more.
Ocean salinity has an important impact on marine environment simulations. The Soil Moisture and Ocean Salinity (SMOS) mission is the first satellite in the world to provide large-scale global salinity observations of the oceans. Salinity remote sensing observations in the open ocean have been successfully applied in data assimilations, while SMOS salinity observations contain large errors in the coastal ocean (including the South China Sea (SCS)) and high latitudes and cannot be effectively applied in ocean data assimilations. In this paper, the SMOS salinity observation data are corrected with the Generalized Regression Neural Network (GRNN) in data assimilation preprocessing, which shows that after correction, the bias and root mean square error (RMSE) of the SMOS sea surface salinity (SSS) compared with the Argo observations can be reduced from 0.155 PSU and 0.415 PSU to −0.003 PSU and 0.112 PSU, respectively, in the South China Sea. The effect is equally significant in the northwestern Pacific region. The preprocessed salinity data were applied to an assimilation in a coastal region for the first time. The six groups of assimilation experiments set in the South China Sea showed that the assimilation of corrected SMOS SSS can effectively improve the upper ocean salinity simulation. Full article
(This article belongs to the Special Issue AI-based Remote Sensing Oceanography)
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Open AccessArticle
Comparison of Machine Learning Algorithms for Retrieval of Water Quality Indicators in Case-II Waters: A Case Study of Hong Kong
Remote Sens. 2019, 11(6), 617; https://doi.org/10.3390/rs11060617
Received: 15 February 2019 / Accepted: 7 March 2019 / Published: 13 March 2019
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Abstract
Anthropogenic activities in coastal regions are endangering marine ecosystems. Coastal waters classified as case-II waters are especially complex due to the presence of different constituents. Recent advances in remote sensing technology have enabled to capture the spatiotemporal variability of the constituents in coastal [...] Read more.
Anthropogenic activities in coastal regions are endangering marine ecosystems. Coastal waters classified as case-II waters are especially complex due to the presence of different constituents. Recent advances in remote sensing technology have enabled to capture the spatiotemporal variability of the constituents in coastal waters. The present study evaluates the potential of remote sensing using machine learning techniques, for improving water quality estimation over the coastal waters of Hong Kong. Concentrations of suspended solids (SS), chlorophyll-a (Chl-a), and turbidity were estimated with several machine learning techniques including Artificial Neural Network (ANN), Random Forest (RF), Cubist regression (CB), and Support Vector Regression (SVR). Landsat (5,7,8) reflectance data were compared with in situ reflectance data to evaluate the performance of machine learning models. The highest accuracies of the water quality indicators were achieved by ANN for both, in situ reflectance data (89%-Chl-a, 93%-SS, and 82%-turbidity) and satellite data (91%-Chl-a, 92%-SS, and 85%-turbidity. The water quality parameters retrieved by the ANN model was further compared to those retrieved by “standard Case-2 Regional/Coast Colour” (C2RCC) processing chain model C2RCC-Nets. The root mean square errors (RMSEs) for estimating SS and Chl-a were 3.3 mg/L and 2.7 µg/L, respectively, using ANN, whereas RMSEs were 12.7 mg/L and 12.9 µg/L for suspended particulate matter (SPM) and Chl-a concentrations, respectively, when C2RCC was applied on Landsat-8 data. Relative variable importance was also conducted to investigate the consistency between in situ reflectance data and satellite data, and results show that both datasets are similar. The red band (wavelength ≈ 0.665 µm) and the product of red and green band (wavelength ≈ 0.560 µm) were influential inputs in both reflectance data sets for estimating SS and turbidity, and the ratio between red and blue band (wavelength ≈ 0.490 µm) as well as the ratio between infrared (wavelength ≈ 0.865 µm) and blue band and green band proved to be more useful for the estimation of Chl-a concentration, due to their sensitivity to high turbidity in the coastal waters. The results indicate that the NN based machine learning approaches perform better and, thus, can be used for improved water quality monitoring with satellite data in optically complex coastal waters. Full article
(This article belongs to the Special Issue AI-based Remote Sensing Oceanography)
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Open AccessArticle
Ship Classification Based on Multifeature Ensemble with Convolutional Neural Network
Remote Sens. 2019, 11(4), 419; https://doi.org/10.3390/rs11040419
Received: 7 January 2019 / Revised: 10 February 2019 / Accepted: 12 February 2019 / Published: 18 February 2019
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Abstract
As an important part of maritime traffic, ships play an important role in military and civilian applications. However, ships’ appearances are susceptible to some factors such as lighting, occlusion, and sea state, making ship classification more challenging. This is of great importance when [...] Read more.
As an important part of maritime traffic, ships play an important role in military and civilian applications. However, ships’ appearances are susceptible to some factors such as lighting, occlusion, and sea state, making ship classification more challenging. This is of great importance when exploring global and detailed information for ship classification in optical remote sensing images. In this paper, a novel method to obtain discriminative feature representation of a ship image is proposed. The proposed classification framework consists of a multifeature ensemble based on convolutional neural network (ME-CNN). Specifically, two-dimensional discrete fractional Fourier transform (2D-DFrFT) is employed to extract multi-order amplitude and phase information, which contains such important information as profiles, edges, and corners; completed local binary pattern (CLBP) is used to obtain local information about ship images; Gabor filter is used to gain the global information about ship images. Then, deep convolutional neural network (CNN) is applied to extract more abstract features based on the above information. CNN, extracting high-level features automatically, has performed well for object classification tasks. After high-feature learning, as the one of fusion strategies, decision-level fusion is investigated for the final classification result. The average accuracy of the proposed approach is 98.75% on the BCCT200-resize data, 92.50% on the original BCCT200 data, and 87.33% on the challenging VAIS data, which validates the effectiveness of the proposed method when compared to the existing state-of-art algorithms. Full article
(This article belongs to the Special Issue AI-based Remote Sensing Oceanography)
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Other

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Open AccessLetter
A Data-Driven Approach to Classifying Wave Breaking in Infrared Imagery
Remote Sens. 2019, 11(7), 859; https://doi.org/10.3390/rs11070859
Received: 28 March 2019 / Revised: 1 April 2019 / Accepted: 5 April 2019 / Published: 10 April 2019
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Abstract
We apply deep convolutional neural networks (CNNs) to estimate wave breaking type (e.g., non-breaking, spilling, plunging) from close-range monochrome infrared imagery of the surf zone. Image features are extracted using six popular CNN architectures developed for generic image feature extraction. Logistic regression on [...] Read more.
We apply deep convolutional neural networks (CNNs) to estimate wave breaking type (e.g., non-breaking, spilling, plunging) from close-range monochrome infrared imagery of the surf zone. Image features are extracted using six popular CNN architectures developed for generic image feature extraction. Logistic regression on these features is then used to classify breaker type. The six CNN-based models are compared without and with augmentation, a process that creates larger training datasets using random image transformations. The simplest model performs optimally, achieving average classification accuracies of 89% and 93%, without and with image augmentation respectively. Without augmentation, average classification accuracies vary substantially with CNN model. With augmentation, sensitivity to model choice is minimized. A class activation analysis reveals the relative importance of image features to a given classification. During its passage, the front face and crest of a spilling breaker are more important than the back face. For a plunging breaker, the crest and back face of the wave are most important, which suggests that CNN-based models utilize the distinctive ‘streak’ temperature patterns observed on the back face of plunging breakers for classification. Full article
(This article belongs to the Special Issue AI-based Remote Sensing Oceanography)
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