Quantifying the spatial ripple effect of the Bohai Sea ice disaster in the winter of 2009/2010 in 31 provinces of China

Wang, Cailin; Wu, Jidong; He, Xin; Ye, Mengqi; Liu, Yu

doi:10.1080/19475705.2018.1489312

Cited by 7 publications

(5 citation statements)

References 47 publications

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“…First, assume that, at time t, the first derivative of the objective function for the parameter is g t . Equations (5) and (6) are updates to the biased first-order moment estimate and the biased second-order moment estimate of the gradient. The deviation is corrected according to the gradient by Equations 7and (8), and, lastly, the parameter update is completed by Equation 9.…”

Section: D-cnnmentioning

confidence: 99%

“…An unusually large flux of polar sea ice will disrupt the balance of freshwater [4], and affect the survival of living things [5]. Sea ice at a mid-high latitude affects the human marine fishery [6], coastal construction industry, and manufacturing industry, and also causes serious economic losses [7]. In recent years, sea ice disasters have attracted more attention.…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral Sea Ice Image Classification Based on the Spectral-Spatial-Joint Feature with Deep Learning

et al. 2019

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Sea ice is one of the causes of marine disasters. The classification of sea ice images is an important part of sea ice detection. The labeled samples in hyperspectral sea ice image classification are difficult to acquire, which causes minor sample problems. In addition, most of the current sea ice classification methods mainly use spectral features for shallow learning, which also limits further improvement of the sea ice classification accuracy. Therefore, this paper proposes a hyperspectral sea ice image classification method based on the spectral-spatial-joint feature with deep learning. The proposed method first extracts sea ice texture information by the gray-level co-occurrence matrix (GLCM). Then, it performs dimensionality reduction and a correlation analysis of the spectral information and spatial information of the unlabeled samples, respectively. It eliminates redundant information by extracting the spectral-spatial information of the neighboring unlabeled samples of the labeled sample and integrating the information with the spectral and texture data of the labeled sample to further enhance the quality of the labeled sample. Lastly, the three-dimensional convolutional neural network (3D-CNN) model is designed to extract the deep spectral-spatial features of sea ice. The proposed method combines relevant textural features and performs spectral-spatial feature extraction based on the 3D-CNN model by using a large amount of unlabeled sample information. In order to verify the effectiveness of the proposed method, sea ice classification experiments are carried out on two hyperspectral data sets: Baffin Bay and Bohai Bay. Compared with the CNN algorithm based on a single feature (spectral or spatial) and other CNN algorithms based on spectral-spatial features, the experimental results show that the proposed method achieves better sea ice classification (98.52% and 97.91%) with small samples. Therefore, it is more suitable for classifying hyperspectral sea ice images.

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Section: D-cnnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hyperspectral Sea Ice Image Classification Based on the Spectral-Spatial-Joint Feature with Deep Learning

et al. 2019

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“…3 Therefore, sea ice detection has valuable research significance, and sea ice classification provides important information. 4 In particular, correctly distinguishing the type of sea ice is significant for assessing and predicting sea ice conditions.…”

Section: Introductionmentioning

confidence: 99%

“…Sea ice has important impacts on the global climate, heat balance, and water balance; 2 on the other hand, sea ice poses serious threats to ship navigation, seabed mining, and polar ocean surveys 3 . Therefore, sea ice detection has valuable research significance, and sea ice classification provides important information 4 . In particular, correctly distinguishing the type of sea ice is significant for assessing and predicting sea ice conditions.…”

Section: Introductionmentioning

confidence: 99%

Remote sensing sea ice classification based on DenseNet and heterogeneous data fusion

Han

Shen

Hong

et al. 2022

J. Appl. Rem. Sens.

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.Most remote sensing sea ice classification methods use single-source remote sensing data, such as synthetic aperture radar (SAR) data and optical remote sensing data. SAR data contain rich sea ice texture information, but the data are relatively single, making it difficult to distinguish detailed sea ice categories. Optical data include abundant spatial-spectral information, but they are often affected by clouds, fog, and severe weather. Hence, given the limitations of single-source data, the remote sensing sea ice classification accuracy cannot be further improved. A sea ice classification method based on deep learning and multisource remote sensing data fusion is proposed utilizing an improved densely connected convolutional neural network (DenseNet) to mine and fuse the multilevel features of sea ice. According to the characteristics of SAR data and optical data, a dual-branch network structure based on an improved DenseNet is employed for feature extraction, and a squeeze-and-excitation attention mechanism is introduced to weight the fusion features to further enhance the feature weights that can effectively distinguish different types of sea ice. The fully connected network is used to perform the deep fusion of features and classify sea ice. To verify the effectiveness of the proposed method, two sets of sea ice data are utilized for sea ice classification. The experimental results show that the proposed method fully excavates and fuses the multilevel characteristics of heterogeneous data using the improved dual-branch network structure, leverages the complementary characteristics of SAR data and optical data, significantly increases the sea ice classification accuracy, and effectively improves the influence of cloud cover on the sea ice classification accuracy. Compared with the typical single-source data classification method and other heterogeneous data fusion classification methods, the proposed method achieves superior overall classification accuracy (98.49% and 98.58%).

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“…ARIO is now a well-established and a pivotal model in its field, has been used in multiple studies, and has seen several extensions or adaptations (Guan et al, 2020;Hallegatte, 2008Hallegatte, , 2013Hallegatte et al, 2010;Henriet et al, 2012;Jenkins, 2013;E. E. Koks et al, 2015;Ranger et al, 2010;C. Wang et al, 2018;Wu et al, 2011).…”

mentioning

confidence: 99%

BoARIO: A Python package implementing the ARIO indirect economic cost model

Juhel

2024

JOSS

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Quantifying the spatial ripple effect of the Bohai Sea ice disaster in the winter of 2009/2010 in 31 provinces of China

Cited by 7 publications

References 47 publications

Hyperspectral Sea Ice Image Classification Based on the Spectral-Spatial-Joint Feature with Deep Learning

Hyperspectral Sea Ice Image Classification Based on the Spectral-Spatial-Joint Feature with Deep Learning

Remote sensing sea ice classification based on DenseNet and heterogeneous data fusion

BoARIO: A Python package implementing the ARIO indirect economic cost model

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