CloudNet: Ground‐Based Cloud Classification With Deep Convolutional Neural Network

Zhang, Jinglin; Liu, Pu; Zhang, Feng; Song, Qianqian

doi:10.1029/2018gl077787

Cited by 184 publications

(139 citation statements)

References 28 publications

(44 reference statements)

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“…For the DCNN models, an advanced type of deep learning models, the features useful for robust classification are dug out from data, instead of being predefined. This idea is repeatedly verified in the computer vision community (Russakovsky et al, ) as well as in the geophysics community (Reichstein et al, ; J. Zhang et al, ; Anantrasirichai et al, ). There are several existing studies for DCNN‐based flooding mapping.…”

Section: Introductionmentioning

confidence: 78%

Coastal Inundation Mapping From Bitemporal and Dual‐Polarization SAR Imagery Based on Deep Convolutional Neural Networks

Liu

Zheng

2019

JGR Oceans

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This study develops an effective and robust method to mine bitemporal and dual-polarization synthetic aperture radar (SAR) imagery information for coastal inundation mapping, based on deep convolutional neural networks. The specially tailored deep convolutional neural network-based SAR coastal flooding mapping network (SARCFMNet) leverages two modifications to improve the accuracy and robustness: the physics-aware input information design and the regularization. The proposed SARCFMNet is applied to the mapping and impact analysis of the coastal inundation caused by 2017 Hurricane Harvey near Houston, Texas, USA. Six pairs of Sentinel-1 SAR images are analyzed along with corresponding ground truth data from Copernicus Emergency Management Service Rapid Mapping products and land-cover types from Google Earth and OpenStreetMap. Flooded areas of 4,000 km 2 are extracted and analyzed. In an analyzed scene, 76% of the flooded area was agriculture area like pasture and cultivated crops field. A flooding map series shows the inundation shrinking rate is about 1% of the analyzed scene per day after the passage of Harvey. However, there was a delayed inundation in Glen Flora, Texas, after the heavy raining period. The average mapping accuracy and F1 score, that is, the harmonic mean of recall and precision, are 0.98 and 0.88, respectively. The impact of wind and cost-sensitive loss functions on the development of SARCFMNet is also discussed. This study demonstrates the proposed method can accurately map hurricane-induced inundation. The method can also be readily extended to other multitemporal SAR imagery classification applications. Plain Language SummaryCoastal flooding caused by hurricanes has a huge impact on the safety and properties of people in coastal areas. Coastal flooding mapping using synthetic aperture radar (SAR) data is a low-cost and wide-coverage means; moreover, SAR has day-and-night, all-weather observation abilities. High-accuracy and robust coastal flooding mapping from SAR imagery information will help the management to formulate more targeted responses and the researchers to better understand coastal flooding mechanisms and develop more precise forecasting models. Based on deep convolutional neural networks, a specially tailored SAR coastal flooding mapping network method is developed to mine bitemporal and dual-polarization SAR imagery information for coastal flooding mapping. The performance of the proposed SAR coastal flooding mapping network is demonstrated by mapping the coastal flooding near Houston, Texas, USA, after 2017 Hurricane Harvey. The study verifies that the mapping accuracy and robustness of the proposed method are better than those of the classic and widely applied deep convolutional neural network method and also shows the mapping products contribute to a better understanding of the geospatial and temporal characteristics of coastal flooding.

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Section: Introductionmentioning

confidence: 78%

Coastal Inundation Mapping From Bitemporal and Dual‐Polarization SAR Imagery Based on Deep Convolutional Neural Networks

Liu

Zheng

2019

JGR Oceans

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“…Cloud height, cloud coverage, and cloud type are three major aspects of cloud observation and have been extensively studied (Davies, ; Fu et al, ; Liu et al, ; Zhang et al, ; Zhou et al, ). However, due to the variability and diversity of cloud appearances, cloud type classification is extremely challenging and still under development.…”

Section: Introductionmentioning

confidence: 99%

Ground‐Based Cloud Classification Using Task‐Based Graph Convolutional Network

Liu

Zhong

et al. 2020

Geophysical Research Letters

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Clouds play a significant role in weather forecasts, water cycle, and climate system. However, existing methods ignore the relations of ground‐based cloud images. In this letter, we propose a novel method named task‐based graph convolutional network (TGCN) for ground‐based cloud classification, which takes image relations into consideration. To this end, we construct the graph using convolutional neural network‐based features of ground‐based cloud images which are learned in a supervised manner, and incorporate the graph computation into TGCN. Given that existing ground‐based cloud databases are with limited labeled training images and categorized according to different classification criteria, we release the largest ground‐based remote sensing cloud database (GRSCD) to provide a comparative study for different methods and to further improve the study of regional sky conditions. The experimental results on GRSCD manifest the effectiveness of TGCN for ground‐based cloud classification.

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“…In recent years, deep-learning methods have also been applied to the meteorology. Ground-based cloud and ice crystal are classified through various deeplearning models Zhang et al, 2018;Xiao et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…From a cloud-physics perspective, it is important to distinguish between convective and stratiform precipitation, which are characterized by different precipitation growth mechanisms (Houghton, 1968). Ground-based cloud and ice crystal are classified through various deeplearning models Zhang et al, 2018;Xiao et al, 2019). Ground-based cloud and ice crystal are classified through various deeplearning models Zhang et al, 2018;Xiao et al, 2019).…”

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confidence: 99%

An Automatic Rainfall‐Type Classification Algorithm Combining Diverse 3‐D Features of Radar Echoes

Lei

Yang

et al. 2019

Earth and Space Science

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This paper proposes an improved algorithm which combines neural networks with diverse 3-D structural features to partition radar reflectivity into convective and stratiform precipitation types. Radar data used in this work were obtained from three networked X-band Doppler radars located in Chengdu, China. The proposed algorithm consists of the two sections: six high-resolution features, which could be extracted from radar volume scanning and expressed the characteristics of the target in many ways, are selected as input to the neural network; systematic self-diagnosis is implemented; and the optimized model is determined according to analysis of bias and variance of classifier. Three state-of-art classification algorithms were implemented as references for algorithm evaluation. Both subjective comparison and statistical results convince that performance of the proposed algorithm is better than performance of traditional classification algorithms in various weather systems. The statistical results show that the proposed algorithm F-score values are 3%, 24%, and 30% higher than the fuzzy logic, SHY95, and BL algorithms and the recognition speed of the proposed algorithm is 54 times, two times, and four times that of fuzzy logic, SHY95, and BL, respectively. Considering network training is an offline procedure, classification by proposed neural network algorithm has great potential in real-time weather analysis for the precipitation classification. Plain Language SummaryIn recent years, networked radar become the popular way to overcome some of the inherent shortcomings of single radar. There are different scanning strategies for convective and stratiform clouds, and it is difficult to accurately distinguish between convective clouds and stratiform clouds, and when the accuracy is high, there will be a problem of low-recognition rate. Our algorithm utilizes a variety of features derived from radar products that have a good distinction between the two categories. At the same time, combined with neural network, its powerful nonlinear fitting and high speed make the recognition speed fast and the recognition result is high. The recognition result of our algorithm is higher than the recognition result of the popular fuzzy logic algorithm, and its recognition speed is even several times that of the fuzzy logic algorithm.

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CloudNet: Ground‐Based Cloud Classification With Deep Convolutional Neural Network

Cited by 184 publications

References 28 publications

Coastal Inundation Mapping From Bitemporal and Dual‐Polarization SAR Imagery Based on Deep Convolutional Neural Networks

Coastal Inundation Mapping From Bitemporal and Dual‐Polarization SAR Imagery Based on Deep Convolutional Neural Networks

Ground‐Based Cloud Classification Using Task‐Based Graph Convolutional Network

An Automatic Rainfall‐Type Classification Algorithm Combining Diverse 3‐D Features of Radar Echoes

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