Two-Dimensional Hydrometeor Image Classification by Statistical Pattern Recognition Algorithms

Rahman, Mizanur; Jacquot, Raymond G.; Quincy, E.; Stewart, Ronald E.

doi:10.1175/1520-0450(1981)020<0536:tdhicb>2.0.co;2

Cited by 6 publications

(3 citation statements)

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“…Traditional methods require considerable time and effort and rely on much subjective empirical knowledge, which leads to inconsistencies and deviations. In addition, traditional methods of automatic classification of ice crystal particles are based on ice crystal particle physical properties, such as particle radius and circumference, to distinguish different categories, such as statistical recognition methods based on probability [1][2][3] and parameter identification methods based on particle image geometric characteristics [4,5]. Traditional physical methods are classified according to threshold setting and the subjective experience of experts.…”

Section: Introductionmentioning

confidence: 99%

A Hypergraph-Embedded Convolutional Neural Network for Ice Crystal Particle Habit Classification

Liao¹,

Duan²,

Zhang³

et al. 2021

Intelligent Automation &Amp; Soft Computing

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In the field of weather modification, it is important to accurately identify the ice crystal particles in ice clouds. When ice crystal habits are correctly identified, cloud structure can be further understood and cloud seeding and other methods of weather modification can be used to change the microstructure of the cloud. Consequently, weather phenomena can be changed at an appropriate time to support human production and quality of life. However, ice crystal morphology is varied. Traditional ice crystal particle classification methods are based on expert experience, which is subjective and unreliable for the identification of the categories by threshold setting. In addition, existing deep learning methods are faced with the problem of improving classification performance on datasets with unbalanced sample distributions. Therefore, we designed a Convolutional Neural Network (CNN) embedded with a hypergraph convolution module, named Hy-INet. The hypergraph convolution module can effectively capture information from hypergraphs constructed from local and global feature spaces and learn the features of small samples in ice crystal datasets that have unbalanced sample numbers. Experimental results demonstrate that the proposed method can achieve superior performance in the classification task of ice crystal particle habits.

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Section: Introductionmentioning

confidence: 99%

A Hypergraph-Embedded Convolutional Neural Network for Ice Crystal Particle Habit Classification

Liao¹,

Duan²,

Zhang³

et al. 2021

Intelligent Automation &Amp; Soft Computing

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“…In the research of cloud precipitation particle shape recognition algorithm, used adaptive Kalman filter method and Bayesian decision theory to classify cloud particle images into 7 categories based on cloud particle images collected by 2DC probes [3,4] . Hunter et al (1984) used the empirical orthogonal function to extract the feature values of the particle images, screened the feature values of the particles through the ADAPT algorithm, and linked the screened feature values with six particle shape types.…”

Section: Introductionmentioning

confidence: 99%

Research on recognition method of cloud precipitation particle shape based on BP neural network

2021

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In order to solve the problem that the shape of cloud particle images measured by airborne cloud imaging probe (CIP) cannot be automatically recognized, this paper proposes an automatic recognition method of cloud and precipitation particle shape based on BP neural network. This method mainly uses a set of geometric parameters which can better describe the shape characteristics of cloud precipitation particles. Based on the cloud precipitation particle images measured by CIP in the precipitation stratiform clouds in northern China, a particle shape data training set and a testing set were constructed to train and verify the effect of the selected BP neural network model. The selected BP neural network model can classify the cloud particle image into tiny, column, needle, dendrite, aggregate, graupel, sphere, hexagonal and irregular. Utilizing the field campaign data measured by CIP, the habit identified results by the improved Holroyd method and by the selected BP neural network model were compared, which shows that the accuracy of BP neural network method is better than that of improved Holroyd method.

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“…Hydrometeor classification algorithms have been the subject of extensive research with previous classification methods ranging from the use of pattern recognition (Rahman et al 1981) to fuzzy logic (Liu and Chandrasekar 2000) to rules based on conceptual models (Park et al 2009). The machine intelligence approach pioneered in the competition is a fundamentally different way to address hydrometeor classification.…”

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confidence: 99%

Reaching Scientific Consensus Through a Competition

Lakshmanan¹,

Elmore²,

Richman³

2010

Bull. Amer. Meteor. Soc.

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Two-Dimensional Hydrometeor Image Classification by Statistical Pattern Recognition Algorithms

Cited by 6 publications

References 0 publications

A Hypergraph-Embedded Convolutional Neural Network for Ice Crystal Particle Habit Classification

A Hypergraph-Embedded Convolutional Neural Network for Ice Crystal Particle Habit Classification

Research on recognition method of cloud precipitation particle shape based on BP neural network

Reaching Scientific Consensus Through a Competition

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