Ocean Remote Sensing Techniques and Applications: A Review (Part II)

Amani, Meisam; Mehravar, Saeed; Asiyabi, Reza Mohammadi; Moghimi, Armin; Ghorbanian, Arsalan; Ahmadi, Seyed Ali; Ebrahimy, Hamid; Moghaddam, Sayyed Hamed Alizadeh; Naboureh, Amin; Ranjgar, Babak; Mohseni, Farzane; Nazari, Mohsen Eslami; Mahdavi, Sahel; Mirmazloumi, S. Mohammad; Ojaghi, Saeid; Jin, Shuanggen

doi:10.3390/w14213401

Cited by 16 publications

(11 citation statements)

References 355 publications

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“…Further combined with passive ocean color remote sensing and in situ biogeochemical buoys (Biogeochemical Argo, BGC-ARGO) and other observation means, it is expected to achieve the threedimensional detection and high-precision inversion of bio-optical and physical parameters in four-dimensional space time within the ~100-m depth of the global ocean for the first time (Chen et al, 2021b). For a more intuitive understanding of the abovementioned spaceborne sensor, the characteristics of these satellite sensors are collected and summarized in Table 1 (Amani et al, 2021;Amani et al, 2022a;Amani et al, 2022b;Amani et al, 2022c).…”

Section: Satellite Remote Sensing In the ~100-m Oceanmentioning

confidence: 99%

Deep blue artificial intelligence for knowledge discovery of the intermediate ocean

et al. 2023

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Oceans at a depth ranging from ~100 to ~1000-m (defined as the intermediate water here), though poorly understood compared to the sea surface, is a critical layer of the Earth system where many important oceanographic processes take place. Advances in ocean observation and computer technology have allowed ocean science to enter the era of big data (to be precise, big data for the surface layer, small data for the bottom layer, and the intermediate layer sits in between) and greatly promoted our understanding of near-surface ocean phenomena. During the past few decades, however, the intermediate ocean is also undergoing profound changes because of global warming, the research and prediction of which are of intensive concern. Due to the lack of three-dimensional ocean theories and field observations, how to remotely sense the intermediate ocean from space becomes a very attractive but challenging scientific issue. With the rapid development of the next generation of information technology, artificial intelligence (AI) has built a new bridge from data science to marine science (called Deep Blue AI, DBAI), which acts as a powerful weapon to extend the paradigm of modern oceanography in the era of the metaverse. This review first introduces the basic prior knowledge of water movement in the ~100 m ocean and vertical stratification within the ~1000-m depths as well as the data resources provided by satellite remote sensing, field observation, and model reanalysis for DBAI. Then, three universal DBAI methodologies, namely, associative statistical, physically informed, and mathematically driven neural networks, are elucidated in the context of intermediate ocean remote sensing. Finally, the unique advantages and potentials of DBAI in data mining and knowledge discovery are demonstrated in a top-down way of “surface-to-interior” via several typical examples in physical and biological oceanography.

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Section: Satellite Remote Sensing In the ~100-m Oceanmentioning

confidence: 99%

Deep blue artificial intelligence for knowledge discovery of the intermediate ocean

et al. 2023

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“…It helps with collision prevention, route optimization, and improves situational awareness to better manage vessel traffic in ice-infested waters. This ensures secure and effective maritime operations [1]. Synthetic Aperture Radar (SAR) is an active remote sensing technique that employs radar signals to generate high-resolution images of the Earth's surface [2].…”

Section: Introductionmentioning

confidence: 99%

“…1 Layer's names are taken from TensorFlow models.Remote Sens. 2023, 15, x FOR PEER REVIEW 8 of 20 best 300 features out of the total of 6912, considering those with the highest mutual information values.…”

mentioning

confidence: 99%

Enhanced Ship/Iceberg Classification in SAR Images Using Feature Extraction and the Fusion of Machine Learning Algorithms

Jafari,

Karami,

Taylor

et al. 2023

Remote Sensing

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Drifting icebergs present significant navigational and operational risks in remote offshore regions, particularly along the East Coast of Canada. In such areas with harsh weather conditions, traditional methods of monitoring and assessing iceberg-related hazards, such as aerial reconnaissance and shore-based support, are often unfeasible. As a result, satellite-based monitoring using Synthetic Aperture Radar (SAR) imagery emerges as a practical solution for timely and remote iceberg classifications. We utilize the C-CORE/Statoil dataset, a labeled dataset containing both ship and iceberg instances. This dataset is derived from dual-polarized Sentinel-1. Our methodology combines state-of-the-art deep learning techniques with comprehensive feature selection. These features are coupled with machine learning algorithms (neural network, LightGBM, and CatBoost) to achieve accurate and efficient classification results. By utilizing quantitative features, we capture subtle patterns that enhance the model’s discriminative capabilities. Through extensive experiments on the provided dataset, our approach achieves a remarkable accuracy of 95.4% and a log loss of 0.11 in distinguishing icebergs from ships in SAR images. The introduction of additional ship images from another dataset can further enhance both accuracy and log loss results to 96.1% and 0.09, respectively.

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“…Marine habitats have many benefits to the environment and humans. For example, they provide shelter and food for various aquatic species [1][2][3][4]. Moreover, the fishing and tourism and transportation industries largely benefit from marine ecosystems [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…Various remote sensing systems, such as optical satellites, bathymetric Light Detection and Ranging (LiDAR), Sound Navigation And Ranging (SONAR), and drones, provide valuable geospatial datasets to facilitate marine habitat studies over large and remote ocean environments with a minimum cost and over a minimal amount of time [1][2][3][4]11]. For example, airborne bathymetric LiDAR systems have widely been applied to classify marine habitats due to their ability to generate high-density point cloud data over relatively deep-water bodies compared to other airborne and spaceborne remote sensing systems, such as multispectral and Synthetic Aperture Radar (SAR) sensors [7,[12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Marine Habitat Mapping Using Bathymetric LiDAR Data: A Case Study from Bonne Bay, Newfoundland

Amani¹,

MacDonald²,

Salehitangrizi

et al. 2022

Water

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Marine habitats provide various benefits to the environment and humans. In this regard, an accurate marine habitat map is an important component of effective marine management. Newfoundland’s coastal area is covered by different marine habitats, which should be correctly mapped using advanced technologies, such as remote sensing methods. In this study, bathymetric Light Detection and Ranging (LiDAR) data were applied to accurately discriminate different habitat types in Bonne Bay, Newfoundland. To this end, the LiDAR intensity image was employed along with an object-based Random Forest (RF) algorithm. Two types of habitat classifications were produced: a two-class map (i.e., Vegetation and Non-Vegetation) and a five-class map (i.e., Eelgrass, Macroalgae, Rockweed, Fine Sediment, and Gravel/Cobble). It was observed that the accuracies of the produced habitat maps were reasonable considering the existing challenges, such as the error of the LiDAR data and lacking enough in situ samples for some of the classes such as macroalgae. The overall classification accuracies for the two-class and five-class maps were 87% and 80%, respectively, indicating the high capability of the developed machine learning model for future marine habitat mapping studies. The results also showed that Eelgrass, Fine Sediment, Gravel/Cobble, Macroalgae, and Rockweed cover 22.4% (3.66 km2), 51.4% (8.39 km2), 13.5% (2.21 km2), 6.9% (1.12 km2), and 5.8% (0.95 km2) of the study area, respectively.

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Ocean Remote Sensing Techniques and Applications: A Review (Part II)

Cited by 16 publications

References 355 publications

Deep blue artificial intelligence for knowledge discovery of the intermediate ocean

Deep blue artificial intelligence for knowledge discovery of the intermediate ocean

Enhanced Ship/Iceberg Classification in SAR Images Using Feature Extraction and the Fusion of Machine Learning Algorithms

Marine Habitat Mapping Using Bathymetric LiDAR Data: A Case Study from Bonne Bay, Newfoundland

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