Cloud Type Classification of Total-Sky Images Using Duplex Norm-Bounded Sparse Coding

2017 ◽  
Vol 10 (7) ◽  
pp. 3360-3372 ◽  
Author(s):  
Jinrui Gan ◽  
Weitao Lu ◽  
Qingyong Li ◽  
Zhen Zhang ◽  
Jun Yang ◽  
...  
Keyword(s):  
Sparse Coding ◽  
Cloud Type ◽  
Type Classification

scholarly journals Improving Cloud Type Classification of Ground-Based Images Using Region Covariance Descriptors

2020 ◽  
Author(s):  
Yuzhu Tang ◽  
Pinglv Yang ◽  
Zeming Zhou ◽  
Jianyu Chen ◽  
Delu Pan ◽  
...  
Keyword(s):  
Weather Forecasting ◽  
Support Vector ◽  
Cloud Type ◽  
Short Term ◽  
Cloud Characteristics ◽  
Region Covariance ◽  
Histogram Feature ◽  
Type Classification ◽  
Huge Challenge

Abstract. Cloud types are important indicators of cloud characteristics and short-term weather forecasting. The meteorological researchers can benefit from the automatic cloud type recognition of massive images captured by the ground-based imagers. However, by far it is still of huge challenge to design a powerful discriminative classifier for cloud categorization. To tackle this difficulty, in this paper, we present an improved method with region covariance descriptors (RCovDs) and Riemannian Bag-of-Feature (BoF). RCovDs model the correlations among different dimensional features, that allows for a more discriminative representation. BoF is extended from Euclidean space to Riemannian manifold by k-Means clustering, in which Stein divergence is adopted as a similarity metric. The histogram feature is extracted by encoding RCovDs of the cloud image blocks with BoF-based codebook. The multi-class support vector machine (SVM) is utilized for the recognition of cloud types. The experiments on the ground-based cloud image datasets validate the proposed method and exhibit the competitive performance against state-of-the-art methods.

scholarly journals Improving cloud type classification of ground-based images using region covariance descriptors

2021 ◽  
Vol 14 (1) ◽  
pp. 737-747
Author(s):  
Yuzhu Tang ◽  
Pinglv Yang ◽  
Zeming Zhou ◽  
Delu Pan ◽  
Jianyu Chen ◽  
...  
Keyword(s):  
Support Vector ◽  
Cloud Type ◽  
Training Samples ◽  
Multiclass Support Vector Machine ◽  
High Prediction ◽  
Classification Of Images ◽  
Region Covariance ◽  
The Relationship ◽  
Type Classification

Abstract. The distribution and frequency of occurrence of different cloud types affect the energy balance of the Earth. Automatic cloud type classification of images continuously observed by the ground-based imagers could help climate researchers find the relationship between cloud type variations and climate change. However, by far it is still a huge challenge to design a powerful discriminative classifier for cloud categorization. To tackle this difficulty, in this paper, we present an improved method with region covariance descriptors (RCovDs) and the Riemannian bag-of-feature (BoF) method. RCovDs model the correlations among different dimensional features, which allows for a more discriminative representation. BoF is extended from Euclidean space to Riemannian manifold by k-means clustering, in which Stein divergence is adopted as a similarity metric. The histogram feature is extracted by encoding RCovDs of the cloud image blocks with a BoF-based codebook. The multiclass support vector machine (SVM) is utilized for the recognition of cloud types. The experiments on the ground-based cloud image datasets show that a very high prediction accuracy (more than 98 % on two datasets) can be obtained with a small number of training samples, which validate the proposed method and exhibit the competitive performance against state-of-the-art methods.

Classifiers for Ground-Based Cloud Images Using Texture Features

2014 ◽  
Vol 931-932 ◽  
pp. 1392-1396 ◽  
Author(s):  
Thitinan Kliangsuwan ◽  
Apichat Heednacram
Keyword(s):  
Nearest Neighbor ◽  
Texture Features ◽  
Correct Classification ◽  
Ann Model ◽  
Cloud Type ◽  
Artificial Neural Network Ann ◽  
Sky Conditions ◽  
Type Classification ◽  
Better Than

The classification of ground-based cloud images has received more attention recently. The result of this work applies to the analysis of climate change; a correct classification is, therefore, important. In this paper, we used 18 texture features to distinguish 7 sky conditions. The important parameters of two classifiers are fine-tuned in the experiment, namely, k-nearest neighbor (k-NN) and artificial neural network (ANN). The performances of the two classifications were compared. Advantages and limitations of both classifiers were discussed. Our result revealed that the k-NN model performed at 72.99% accuracy while the ANN model has higher performance at 86.93% accuracy. We showed that our result is better than previous studies. Finally, seven most effective texture features are recommended to be used in the field of cloud type classification.

Automatic Grain Type Classification of Snow Micro Penetrometer Signals With Random Forests

2013 ◽  
Vol 51 (6) ◽  
pp. 3328-3335 ◽  
Author(s):  
Scott Havens ◽  
Hans-Peter Marshall ◽  
Christine Pielmeier ◽  
Kelly Elder
Keyword(s):  
Random Forests ◽  
Type Classification

Machine Learning techniques for solar irradiation nowcasting: Cloud type classification forecast through satellite data and imagery

2022 ◽  
Vol 305 ◽  
pp. 117834
Author(s):  
Alfredo Nespoli ◽  
Alessandro Niccolai ◽  
Emanuele Ogliari ◽  
Giovanni Perego ◽  
Elena Collino ◽  
...  
Keyword(s):  
Machine Learning ◽  
Satellite Data ◽  
Machine Learning Techniques ◽  
Solar Irradiation ◽  
Cloud Type ◽  
Learning Techniques ◽  
Type Classification

scholarly journals Large-scale unsupervised clustering of Orca vocalizations: a model for describing orca communication systems

2019 ◽  
Author(s):  
Marion Poupard ◽  
Paul Best ◽  
Jan Schlüter ◽  
Helena Symonds ◽  
Paul Spong ◽  
...  
Keyword(s):  
Communication Systems ◽  
Large Scale ◽  
Pitch Contour ◽  
Call Type ◽  
Recording Station ◽  
The Face ◽  
Multichannel Recordings ◽  
Type Classification ◽  
Anthropic Pressure

Killer whales (Orcinus orca) can produce 3 types of signals: clicks, whistles and vocalizations. This study focuses on Orca vocalizations from northern Vancouver Island (Hanson Island) where the NGO Orcalab developed a multi-hydrophone recording station to study Orcas. The acoustic station is composed of 5 hydrophones and extends over 50 km 2 of ocean. Since 2015 we are continuously streaming the hydrophone signals to our laboratory in Toulon, France, yielding nearly 50 TB of synchronous multichannel recordings. In previous work, we trained a Convolutional Neural Network (CNN) to detect Orca vocalizations, using transfer learning from a bird activity dataset. Here, for each detected vocalization, we estimate the pitch contour (fundamental frequency). Finally, we cluster vocalizations by features describing the pitch contour. While preliminary, our results demonstrate a possible route towards automatic Orca call type classification. Furthermore, they can be linked to the presence of particular Orca pods in the area according to the classification of their call types. A large-scale call type classification would allow new insights on phonotactics and ethoacoustics of endangered Orca populations in the face of increasing anthropic pressure.

scholarly journals Cloud Type Classification (cldtype) Value-Added Product

2017 ◽  
Author(s):  
Donna Flynn ◽  
Yan Shi ◽  
K-S Lim ◽  
Laura Riihimaki
Keyword(s):  
Value Added ◽  
Cloud Type ◽  
Type Classification
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