Estimation of Cloud Cover Using Colour and Texture

Richards, K.; Sullivan, G. D.

doi:10.5244/c.6.45

Cited by 7 publications

(9 citation statements)

References 3 publications

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“…Many authors have presented there view of the prediction of rainfall. D.K.Richards and G.D.Sullivan has presented a methodology in which the prediction of the rainfall is based on the discrimination of the cloud for which the classification is done basing on the cloud formation [1]. M.K.Kundu and D.Priyank proposed a multiband wavelet transform for extracting the features from the cloud [2].…”

Section: Related Workmentioning

confidence: 99%

Prediction of Rainfall Using Unsupervised Model based Approach Using K-Means Algorithm

Krishna¹

2015

IJMSC

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Prediction of rainfall has gained a significant importance because of many associated factors like cultivating, aquaculture and other indirect parameters allied with the rainfall like global heat. Therefore it is necessary to predict the rainfall from the satellite images effectively. In this article, a segmentation algorithm is developed based on Gaussian mixture models. The initial parameters are estimated using k-means algorithm. The process is presented by using an 2-fold architecture, where in the first stage database creation is considered and the second stage talks about the prediction. The performance analysis is carried out using metrics like PSNR, IF and MSE. The developed model analyzes the satellite images and predicts the Rainfall efficiently.

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Section: Related Workmentioning

confidence: 99%

Prediction of Rainfall Using Unsupervised Model based Approach Using K-Means Algorithm

Krishna¹

2015

IJMSC

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“…Some of them used the traditional methods like back propagation model of neural network [25] and M-Phonn model [18], while the others included recent developments such as image processing [13], linear regression [6], SLIQ [10], and fuzzy SLIQ [11].…”

Section: Relevant Workmentioning

confidence: 99%

“…K.Richards and G.D. Sullivan combined the features of Bayesian scheme for texture analysis of the cloud images which are taken from the ground [13]. W. Pensuwon, C. Jareanpon, R.J. Frank and N. Davey formed radial basis function neural network with a specially designed genetic algorithm [7].…”

Section: Relevant Workmentioning

confidence: 99%

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Gain Ratio as Attribute Selection Measure in Elegant Decision Tree to Predict Precipitation

Prasad¹,

Naidu

2013

2013 8th EUROSIM Congress on Modelling and Simulation

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Prediction of precipitation is a necessary tool in meteorology. To date, it is technologically and scientifically a challenging task for scientists and researchers around the globe. Rainfall is a liquid form of precipitation that depends primarily on humidity, temperature, pressure, wind speed, dew point, and so on. Because rainfall depends on several parameters, its prediction becomes very complex. Approaches such as the back propagation, linear regression, support vector machine, Bayesian networks, and fuzzy logic can be applied, but their rate of prediction is very low, which leads to unpredictable results. This paper aims at improving the prediction of precipitation compared to Supervised Learning in Quest (SLIQ) decision trees, especially when datasets are large. Because SLIQ decision trees take more computational steps to find split points, they consume more time and thus cannot be applied to huge datasets. An elegant decision tree using gain ratio as an attribute selection measure is adopted, which increases the accuracy rate and decreases the computation time. This approach provides an average accuracy of 76.93% with a reduction of 63% in computational time over SLIQ decision trees.

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“…Therefore, considerable mis‐segmentation between the source domain and the target domains still can be found. Besides, employing the color and texture features can also acquire some promising detection results (Mantelli Neto et al, ; Richards & Sullivan, ), but there are issues that need to be resolved such as smooth regions of cloud and cloud at borders with sky.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Solution for Semantic Segmentation of Three Ground‐based Cloud Datasets

Song

Cui²,

Liu

2020

Earth and Space Science

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The machine learning approach has shown its state-of-the-art ability to handle segmentation and detection tasks. It is increasingly employed to extract patterns and spatiotemporal features from the ever-increasing stream of Earth system data. However, there is still a significant challenge, which is the generalization capability of the model on cloud images in different types and weather conditions. After studying several popular methods, we propose a semantic segmentation neural network for cloud segmentation. It extracts features learned by source and target domains in an end-to-end behavior, which can address the problem of significant lack of labels in the observed cloud image data. It is further evaluated on the Singapore Whole Sky Image Segmentation (SWIMSEG) dataset by using Mean Intersection-over-Union, recall, F-score, and accuracy matrices. The scores of these matrices are 86%, 97%, 92%, and 96%, which prove that it has excellent efficiency and robustness. Most importantly, a new benchmark based on the SWIMSEG dataset for the task of cloud segmentation is introduced. The others, BENCHMARK, Cirrus Cumulus Stratus Nimbus are evaluated through the model trained from the SWIMSEG dataset by way of visualization. Plain Language Summary The machine learning approaches offer a new view about howto effectively and comprehensively understand ground-based cloud datasets. The essential advantage of deep learning methods is that it can extract more critical cloud features automatically than traditional algorithms, such as spatiotemporal features. Therefore, it is worth exploring the possibility of cloud segmentation with the help of deep learning techniques. We first introduce a semantic segmentation neural network for cloud segmentation problems after measuring a few classic neural networks. The results exceed the traditional methods by a large margin with standard evaluation matrices, such as Mean Intersection-over-Union, recall, F-score, and accuracy. The scores achieved here may accomplish as a baseline for competitive development. Second, the trained model is used to produce a few cloud masks in two public datasets: BENCHMARK, Singapore Whole Sky Image Segmentation, and their respective performance is further evaluated. Finally, the segmentation results show the excellent performance and generalization in another untrained dataset, Cirrus Cumulus Stratus Nimbus.

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Estimation of Cloud Cover Using Colour and Texture

Cited by 7 publications

References 3 publications

Prediction of Rainfall Using Unsupervised Model based Approach Using K-Means Algorithm

Prediction of Rainfall Using Unsupervised Model based Approach Using K-Means Algorithm

Gain Ratio as Attribute Selection Measure in Elegant Decision Tree to Predict Precipitation

An Efficient Solution for Semantic Segmentation of Three Ground‐based Cloud Datasets

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