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Special Issue "Remote Sensing in Intelligent Maritime Research"

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

Deadline for manuscript submissions: 15 September 2023 | Viewed by 6853

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Special Issue Editors

Dr. Sheng-Long Kao

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Guest Editor

Department of Transportation Science, National Taiwan Ocean University, Keelung 202301, Taiwan
Interests: transport and communications; electronic nautical instruments; mobile GIS; transportation and environment engineering

Dr. Ming-Feng Yang

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Guest Editor

Department of Transportation Science, National Taiwan Ocean University, Keelung 202301, Taiwan
Interests: warehouse storage management; supply chain management; system simulation; decision support system

Dr. Nan-Jay Su

E-Mail Website
Guest Editor

Department of Environmental Biology and Fisheries Science, National Taiwan Ocean University, Keelung 202301, Taiwan
Interests: fishery oceanography; fishery biology; stock assessment and management; habitat modeling; remote sensing application

Dr. Li-Wen Liao
E-Mail Website
Guest Editor

Ghent Institute for International and European Studies (GIES), Ghent University, 9000 Ghent, Belgium
Interests: international space politics; global space governance; regional space cooperation; space security; space diplomacy; space economy; Taiwan's space policy

Dr. Chen-Joe Fong
E-Mail Website
Guest Editor

National Space Organization, Hsinchu, 30078 Taiwan
Interests: AIS remote sensing; space laser communication; Model-based systems engineering; GNSS radio occultation/reflection; CubeSat and PocketQube development; Hilbert-Huang transformation (HHT) for satellite data trending analysis

Special Issue Information

Dear Colleagues,

To understand future needs and challenges and enhance independent research and development capabilities in the field of marine shipping technology, this Special Issue on “Remote Sensing in Intelligent Maritime Research” aims to collect outstanding studies that contribute to remote sensing and ocean research. This Special Issue considers a number of areas that are important for the development of intelligent maritime technology relating to remote sensing, including, though not restricted to: shipping technology, fishery science, satellite technology, green energy, intelligent technology, and internet of things. We welcome research in marine smart ports and transportation technology, fishery meteorology and the sustainable use and management of aquatic resources, safety analysis of maritime navigation and the identification of ships at sea, and the development of hydrogen fuel and green energy with storage systems.

This Special Issue collects studies on smart transportation systems in ocean, harbor and coastal areas; the use of artificial intelligence, unmanned vehicles, block chain and other intelligent technologies; as well as research based on the technology framework of cyber–physical systems and the internet of things. We hope to provide a compilation of current state-of-the-art and future perspectives related to multiple remote sensing technologies and relevant applications and studies in intelligent maritime development, and integrated analyses that use big data as a database for remote monitoring, telemetry, emergency accident prevention and the effective management of marine areas:

Shipping and satellite technology
Smart transportation systems
Fishery oceanography and meteorology
Sustainable management of aquatic resources
Green energy and storage system
Artificial intelligence and unmanned vehicles
Intelligent technology and internet of things.

Dr. Sheng-Long Kao
Dr. Ming-Feng Yang
Dr. Nan-Jay Su
Dr. Li-Wen Liao
Dr. Chen-Joe Fong
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 2500 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

shipping satellite technology
smart transportation systems
fishery oceanography
sustainable management
green energy
artificial intelligence
Internet of Things

Published Papers (6 papers)

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Research

Open AccessArticle

Low-Delay and Energy-Efficient Opportunistic Routing for Maritime Search and Rescue Wireless Sensor Networks

and

Remote Sens. 2022, 14(20), 5178; https://doi.org/10.3390/rs14205178 - 17 Oct 2022

Cited by 1 | Viewed by 869

Abstract

After the occurrence of a maritime disaster, to save human life and search for important property equipment in the first time, it is indispensable to efficiently transmit search and rescue sea area data to the maritime search and rescue command center (MSRCC) in real-time, so that the MSRCC can make timely and accurate decisions. The key to determining the efficiency of data forwarding is the quality of the routing protocol. Due to the high dynamics of the marine environment and the limited energy of the marine node, the coverage hole and routing path failure problems occur frequently when using the existing routing algorithm for marine data forwarding. Based on the above background, in this work, we study a low-latency and energy-efficient opportunistic routing protocol for maritime search and rescue wireless sensor networks (MSR-WSNs). Considering the adverse impact of wave shadowing on signal transmission, an effective link reliability prediction method is first investigated to quantify the link connectivity among nodes. To mitigate the end-to-end time delay, an optimal expected packet advancement is then derived by combining link con-nectivity with geographic progress threshold

θ

. After that, based on the link connectivity between marine nodes, the optimal expected packet advancement prediction, the distance from the sensing nodes to the sink, and the remaining energy distribution of the nodes, the priority of candidate nodes is calculated and sorted in descending order. Finally, timer-based coordination algorithm is adopted to perform the marine data packet forwarding so as to avoid packet conflict. Computer simulation results demonstrate that compared with benchmark algorithms, the data packet delivery ratio, the delay performance and the average node energy consumption (the average node speed is 20 m/s) of the proposed opportunistic routing protocol are improved by more than 21.4%, 39.2% and 18.1%, respectively. Full article

(This article belongs to the Special Issue Remote Sensing in Intelligent Maritime Research)

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Open AccessArticle

LssDet: A Lightweight Deep Learning Detector for SAR Ship Detection in High-Resolution SAR Images

and

Remote Sens. 2022, 14(20), 5148; https://doi.org/10.3390/rs14205148 - 14 Oct 2022

Viewed by 1035

Abstract

Synthetic aperture radar (SAR) ship detection has been the focus of many previous studies. Traditional SAR ship detectors face challenges in complex environments due to the limitations of manual feature extraction. With the rise of deep learning (DL) techniques, SAR ship detection based on convolutional neural networks (CNNs) has achieved significant achievements. However, research on CNN-based SAR ship detection has mainly focused on improving detection accuracy, and relatively little research has been conducted on reducing computational complexity. Therefore, this paper proposes a lightweight detector, LssDet, for SAR ship detection. LssDet uses Shufflenet v2, YOLOX PAFPN and YOLOX Decopuled Head as the baseline networks, improving based on the cross sidelobe attention (CSAT) module, the lightweight path aggregation feature pyramid network (L-PAFPN) module and the Focus module. Specifically, the CSAT module is an attention mechanism that enhances the model’s attention to the cross sidelobe region and models the long-range dependence between the channel and spatial information. The L-PAFPN module is a lightweight feature fusion network that achieves excellent performance with little computational effort and a low parametric count. The Focus module is a low-loss feature extraction structure. Experiments showed that on the Sar ship detection dataset(SSDD), LssDet’s computational cost was 2.60 GFlops, the model’s volume was 2.25 M and

A P @ [0.5 : 0.95]

was 68.1%. On the Large-scale SAR ship detection dataset-v1.0 (LS-SSDD-v1.0), LssDet’s computational cost was 4.49 GFlops, the model’s volume was 2.25 M and

A P @ [0.5 : 0.95]

was 27.8%. Compared to the baseline network, LssDet had a 3.6% improvement in

A P @ [0.5 : 0.95]

on the SSDD, and LssDet had a 1.5% improvement in

A P @ [0.5 : 0.95]

on the LS-SSDD-v1.0. At the same time, LssDet reduced Floating-point operations per second (Flops) by 7.1% and Paraments (Params) by 23.2%. Extensive experiments showed that LssDet achieves excellent detection results with minimal computational complexity. Furthermore, we investigated the effectiveness of the proposed module through ablation experiments. Full article

(This article belongs to the Special Issue Remote Sensing in Intelligent Maritime Research)

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Open AccessArticle

U-Net for Taiwan Shoreline Detection from SAR Images

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Remote Sens. 2022, 14(20), 5135; https://doi.org/10.3390/rs14205135 - 14 Oct 2022

Viewed by 916

Abstract

Climate change and global warming lead to changes in the sea level and shoreline, which pose a huge threat to island regions. Therefore, it is important to effectively detect the shoreline changes. Taiwan is a typical island, located at the junction of the East China Sea and the South China Sea in the Pacific Northwest, and is deeply affected by shoreline changes. In this research, Taiwan was selected as the study area. In this research, an efficient shoreline detection method was proposed based on the semantic segmentation U-Net model using the Sentinel-1 synthetic aperture radar (SAR) data of Taiwan island. In addition, the batch normalization (BN) module was added to the convolution layers in the U-Net architecture to further improve the generalization ability of U-Net and accelerate the training process. A self-built shoreline dataset was introduced to train the U-Net model and test its detection efficiency. The dataset consists of a total of 4029 SAR images covering all coastal areas of Taiwan. The training samples of the dataset were annotated by morphological processing and manual inspection. The segmentation results of U-Net were then processed by edge detection and morphological postprocessing to extract the shoreline. The experimental results showed that the proposed method could achieve a satisfactory detection performance compared with the related methods using the data provided by the Ministry of the Interior of Taiwan from 2016 to 2019 for different coastal landforms in Taiwan. Within a 5-pixel difference between the detected shoreline and the ground truth data, the F1-Meaure of the proposed method exceeded 80%. In addition, the potential of this method in shoreline change detection was validated with a sandbar located on the southwestern coast of Taiwan. Finally, the entire shoreline of Taiwan has been described by the proposed approach and the detected shoreline length was close to the actual length. Full article

(This article belongs to the Special Issue Remote Sensing in Intelligent Maritime Research)

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Open AccessArticle

Habitat Prediction of Northwest Pacific Saury Based on Multi-Source Heterogeneous Remote Sensing Data Fusion

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Remote Sens. 2022, 14(19), 5061; https://doi.org/10.3390/rs14195061 - 10 Oct 2022

Cited by 2 | Viewed by 905

Abstract

Accurate habitat prediction is important to improve fishing efficiency. Most of the current habitat-prediction studies use the single-source datasets and the sequence model based on single-source datasets, which, to a certain extent, limits the further improvement of prediction accuracy. In this paper, we propose a habitat-prediction method based on the multi-source heterogeneous remote-sensing data fusion, using product-level remote-sensing data and L1B-level original remote-sensing data. We designed a heterogeneous data feature extraction model based on a Convolution Neural Network (CNN) and Long and Short-Term Memory network (LSTM), and we designed a decision-fusion model based on multi-source heterogeneous data feature extraction. In the habitat prediction for the Northwest Pacific Saury, the mean R² of the model reaches 0.9901 and the RMSE decreases to 0.01588 in the model validation experiment. It is significantly better than the results of other models, with the single datasets as input. Moreover, the model performs well in the generalization experiment because we limited the prediction error to less than 8%. Compared with the single-source sequence network model in the existing literature, the proposed method in this paper solves the problem of ineffective fusion caused by the differences in the structure and size of heterogeneous data through multilevel feature fusion and decision fusion, and it deeply explores the features of remote-sensing fishery data with different data structures and sizes. It can effectively improve the accuracy of fishery prediction, proving the feasibility and advancement of using multi-source remote-sensing data for habitat prediction. It also provides new methods and ideas for future research in the field of habitat prediction. Full article

(This article belongs to the Special Issue Remote Sensing in Intelligent Maritime Research)

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Open AccessArticle

A Lightweight Network Based on One-Level Feature for Ship Detection in SAR Images

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Remote Sens. 2022, 14(14), 3321; https://doi.org/10.3390/rs14143321 - 10 Jul 2022

Cited by 5 | Viewed by 1096

Abstract

Recently, deep learning has greatly promoted the development of detection methods for ship targets in synthetic aperture radar (SAR) images. However, existing detection networks are mostly based on large-scale models and high-cost computations, which require high-performance computing equipment to realize real-time processing and limit their hardware transplantation to onboard platforms. To address this problem, a lightweight ship detection network via YOLOX-s is proposed in this paper. Firstly, we remove the computationally heavy pyramidal structure and build a streamlined network based on a one-level feature for higher detection efficiency. Secondly, to expand the limited receptive field and enhance the semantic information of a single-feature map, a residual asymmetric dilated convolution (RADC) block is proposed. Through four branches with different dilation rates, the RADC block can help the detector to capture various ships in complex backgrounds. Finally, to tackle the imbalance problem between ships of different scales in the training stage, we put forward a balanced label assignment strategy called center-based uniform matching. To verify the effectiveness of the proposed method, we conduct extensive experiments on the SAR Ship Detection Dataset (SSDD) and High-Resolution SAR Images Dataset (HRSID). The results show that our method can achieve comparable performance to general detection networks with much less computational cost. Full article

(This article belongs to the Special Issue Remote Sensing in Intelligent Maritime Research)

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Open AccessArticle

Extraction of Floating Raft Aquaculture Areas from Sentinel-1 SAR Images by a Dense Residual U-Net Model with Pre-Trained Resnet34 as the Encoder

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Remote Sens. 2022, 14(13), 3003; https://doi.org/10.3390/rs14133003 - 23 Jun 2022

Cited by 2 | Viewed by 1133

Abstract

Marine floating raft aquaculture (FRA) monitoring is significant for marine ecological environment and food security assessment. Synthetic aperture radar-based monitoring is considered to be an effective means of FRA identification because of its capability for all-weather applications. Considering the poor generalization and extraction accuracy of traditional monitoring methods, a semantic segmentation model called D-ResUnet is proposed to extract FRA areas from Sentinel-1 images. The proposed model has a U-Net-like structure but combines the pre-trained ResNet34 as the encoder and adds dense residual units into the decoder. For this model, the final layer and cropping operation of the original U-Net model are removed to eliminate the model parameters. The mean and standard deviation of Precision, Recall, Intersection over Union (IoU), and F1 score are calculated under a five-fold training strategy to evaluate the model accuracy. The test experiments indicated that the proposed model performs well with the F1 of 92.6% and IoU of 86.24% in FRA extraction tasks. In particular, the ablation experiments and application experiments proved the effectiveness of the improvement strategy and the portability of the proposed D-ResUnet model, respectively. Compared with the other three state-of-the-art semantic segmentation models, the experiments demonstrate a clear accuracy advantage of the D-ResUnet model. For the FRA extraction task, this paper presents a promising approach that has refined extraction capability, high accuracy, and acceptable model complexity. Full article

(This article belongs to the Special Issue Remote Sensing in Intelligent Maritime Research)

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