Deep-learning-based information mining from ocean remote-sensing imagery

Li, Xiaofeng; Liu, Bin; Zheng, Gang; Ren, Yibin; Zhang, Shuangshang; Liu, Yingjie; Gao, Le; Liu, Yuhai; Zhang, Bin; Wang, Fan

doi:10.1093/nsr/nwaa047

Cited by 220 publications

(66 citation statements)

References 121 publications

(115 reference statements)

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“…Ocean remote sensing entering the big-data era (Source: adapted from Li et al, 2020). † Such as those under the UNEP in the Baltic Sea, the North Sea and the NE Atlantic, the Mediterranean, the Red Sea, the Persian Gulf, the waters of the Caribbean, the sea off West Africa and the West Coast of South America, and East Asian waters.…”

Section: Figurementioning

confidence: 99%

Managing Marine Environmental Pollution using Artificial Intelligence

Agarwala

2021

Marit. Technol. Res.

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The marine environment has deteriorated to an extent that it has begun to impact human health and the planet itself. The primary cause of this deterioration as identified are, an increasing population, the industrial revolution and the increased use of fossil fuels. While the damage done to the environment cannot be undone, the impact can be lessened by better understanding the ocean and monitoring future pollution using technology. Such an effort will help achieve sustainability as laid out by the Sustainable Development Goals 2030 of the United Nations. The article aims to provide an insight into one such technology, namely ‘Artificial Intelligence (AI)’, being developed to understand and monitor the marine pollution. In doing so the article will discuss the emerging opportunities and risks associated with the use of AI in managing marine environmental pollution through sustainability. To strengthen the argument, use-cases of AI in the marine environment and their scalability are discussed.

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Section: Figurementioning

confidence: 99%

Managing Marine Environmental Pollution using Artificial Intelligence

Agarwala

2021

Marit. Technol. Res.

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“…In recent years, with the development of remote sensing data, model simulation data, and Argo datasets, multisource sea surface data with a high level of quality have become more and more abundant. Artificial intelligence applications in the oceanography field have been widely studied [19,20]. In particular, machine learning methods based on various sea surface parameters have been used to reconstruct the OST with many outstanding results [6,[21][22][23][24].…”

Section: Related Workmentioning

confidence: 99%

“…Recently, Su et al also estimated the global ocean subsurface temperature and salt anomalies using the extreme gradient boosting (XGBoost) model by combining sea surface remote sensing data and Argo buoy observations [6]. There have been other valuable studies on the ocean's interior that have employed multisource observations [19,20,25].…”

Section: Related Workmentioning

confidence: 99%

Subsurface Temperature Estimation from Sea Surface Data Using Neural Network Models in the Western Pacific Ocean

et al. 2021

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Estimating the ocean subsurface thermal structure (OSTS) based on multisource sea surface data in the western Pacific Ocean is of great significance for studying ocean dynamics and El Niño phenomenon, but it is challenging to accurately estimate the OSTS from sea surface parameters in the area. This paper proposed an improved neural network model to estimate the OSTS from 0–2000 m from multisource sea surface data including sea surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH), and sea surface wind (SSW). In the model experiment, the rasterized monthly average data from 2005–2015 and 2016 were selected as the training and testing set, respectively. The results showed that the sea surface parameters selected in the paper had a positive effect on the estimation process, and the average RMSE value of the ocean subsurface temperature (OST) estimated by the proposed model was 0.55 °C. Moreover, there were pronounced seasonal variation signals in the upper layers (the upper 200 m), however, this signal gradually diminished with increasing depth. Compared with known estimation models such as the random forest (RF), the multiple linear regression (MLR), and the extreme gradient boosting (XGBoost), the proposed model outperformed these models under the data conditions of the paper. This research can provide an advanced artificial intelligence technique for estimating subsurface thermohaline structure in major sea areas.

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“…This approach achieves state-of-the-art results in different applications in remote sensing digital image processing [5]: pan-sharpening [6][7][8][9]; image registration [10][11][12][13], change detection [14][15][16][17], object detection [18][19][20][21], semantic segmentation [22][23][24][25], and time series analysis [26][27][28][29]. The classification algorithms applied in remote sensing imagery uses spatial, spectral, and temporal information to extract characteristics from the targets, where a wide variety of targets show significant results: clouds [30][31][32][33], dust-related air pollutant [34][35][36][37] land-cover/land-use [38][39][40][41], urban features [42][43][44][45], and ocean [46][47][48][49], among others.…”

Section: Introductionmentioning

confidence: 99%

Instance Segmentation for Large, Multi-Channel Remote Sensing Imagery Using Mask-RCNN and a Mosaicking Approach

et al. 2020

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Instance segmentation is the state-of-the-art in object detection, and there are numerous applications in remote sensing data where these algorithms can produce significant results. Nevertheless, one of the main problems is that most algorithms use Red, Green, and Blue (RGB) images, whereas Satellite images often present more channels that can be crucial to improve performance. Therefore, the present work brings three contributions: (a) conversion system from ground truth polygon data into the Creating Common Object in Context (COCO) annotation format; (b) Detectron2 software source code adaptation and application on multi-channel imagery; and (c) large scene image mosaicking. We applied the procedure in a Center Pivot Irrigation System (CPIS) dataset with ground truth produced by the Brazilian National Water Agency (ANA) and Landsat-8 Operational Land Imager (OLI) imagery (7 channels with 30-m resolution). Center pivots are a modern irrigation system technique with massive growth potential in Brazil and other world areas. The round shapes with different textures, colors, and spectral behaviors make it appropriate to use Deep Learning instance segmentation. We trained the model using 512 × 512-pixel sized patches using seven different backbone structures (ResNet50- Feature Pyramid Network (FPN), Resnet50-DC5, ResNet50-C4, Resnet101-FPN, Resnet101-DC5, ResNet101-FPN, and ResNeXt101-FPN). The model evaluation used standard COCO metrics (Average Precision (AP), AP50, AP75, APsmall, APmedium, and AR100). ResNeXt101-FPN had the best results, with a 3% advantage over the second-best model (ResNet101-FPN). We also compared the ResNeXt101-FPN model in the seven-channel and RGB imagery, where the multi-channel model had a 3% advantage, demonstrating great improvement using a larger number of channels. This research is also the first with a mosaicking algorithm using instance segmentation models, where we tested in a 1536 × 1536-pixel image using a non-max suppression sorted by area method. The proposed methodology is innovative and suitable for many other remote sensing problems and medical imagery that often present more channels.

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Deep-learning-based information mining from ocean remote-sensing imagery

Cited by 220 publications

References 121 publications

Managing Marine Environmental Pollution using Artificial Intelligence

Managing Marine Environmental Pollution using Artificial Intelligence

Subsurface Temperature Estimation from Sea Surface Data Using Neural Network Models in the Western Pacific Ocean

Instance Segmentation for Large, Multi-Channel Remote Sensing Imagery Using Mask-RCNN and a Mosaicking Approach

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