Ground-based Cloud Classification Research Articles

CloudDenseNet: Lightweight Ground-Based Cloud Classification Method for Large-Scale Datasets Based on Reconstructed DenseNet.

Cloud observation serves as the fundamental bedrock for acquiring comprehensive cloud-related information. The categorization of distinct ground-based clouds holds profound implications within the meteorological domain, boasting significant applications. Deep learning has substantially improved ground-based cloud classification, with automated feature extraction being simpler and far more accurate than using traditional methods. A reengineering of the DenseNet architecture has given rise to an innovative cloud classification method denoted as CloudDenseNet. A novel CloudDense Block has been meticulously crafted to amplify channel attention and elevate the salient features pertinent to cloud classification endeavors. The lightweight CloudDenseNet structure is designed meticulously according to the distinctive characteristics of ground-based clouds and the intricacies of large-scale diverse datasets, which amplifies the generalization ability and elevates the recognition accuracy of the network. The optimal parameter is obtained by combining transfer learning with designed numerous experiments, which significantly enhances the network training efficiency and expedites the process. The methodology achieves an impressive 93.43% accuracy on the large-scale diverse dataset, surpassing numerous published methods. This attests to the substantial potential of the CloudDenseNet architecture for integration into ground-based cloud classification tasks.

Cloud-MobiNet: An Abridged Mobile-Net Convolutional Neural Network Model for Ground-Based Cloud Classification

More than 60 percent of the global surface is covered by clouds, and they play a vital role in the hydrological circle, climate change, and radiation budgets by modifying shortwaves and longwave. Weather forecast reports are critical to areas such as air and sea transport, energy, agriculture, and the environment. The time has come for artificial intelligence-powered devices to take the place of the current method by which decision-making experts determine cloud types. Convolutional neural network models (CNNs) are starting to be utilized for identifying the types of clouds that are caused by meteorological occurrences. This study uses the publicly available Cirrus Cumulus Stratus Nimbus (CCSN) dataset, which consists of 2543 ground-based cloud images altogether. We propose a model called Cloud-MobiNet for the classification of ground-based clouds. The model is an abridged convolutional neural network based on MobileNet. The architecture of Cloud-MobiNet is divided into two blocks, namely the MobileNet building block and the support MobileNet block (SM block). The MobileNet building block consists of the weights of the depthwise separable convolutions and pointwise separable convolutions of the MobileNet model. The SM block is made up of three dense network layers for feature extraction. This makes the Cloud-MobiNet model very lightweight to be implemented on a smartphone. An overall accuracy success of 97.45% was obtained for the CCSN dataset used for cloud-type classification. Cloud-MobiNet promises to be a significant model in the short term, since automated ground-based cloud classification is anticipated to be a preferred means of cloud observation, not only in meteorological analysis and forecasting but also in the aeronautical and aviation industries.

Voting in Transfer Learning System for Ground-Based Cloud Classification

Cloud classification is a great challenge in meteorological research. The different types of clouds, currently known and present in our skies, can produce radioactive effects that impact the variation of atmospheric conditions, with consequent strong dominance over the earth’s climate and weather. Therefore, identifying their main visual features becomes a crucial aspect. In this paper, the goal is to adopt pretrained deep neural networks-based architecture for clouds image description, and subsequently, classification. The approach is pyramidal. Proceeding from the bottom up, it partially extracts previous knowledge of deep neural networks related to original task and transfers it to the new task. The updated knowledge is integrated in a voting context to provide a classification prediction. The framework trains the neural models on unbalanced sets, a condition that makes the task even more complex, and combines the provided predictions through statistical measures. An experimental phase on different cloud image datasets is performed, and the results achieved show the effectiveness of the proposed approach with respect to state-of-the-art competitors.

Ground-Based Remote Sensing Cloud Classification via Context Graph Attention Network

Most ground-based remote sensing cloud classification methods focus on learning representation features for cloud images while ignoring the correlations among cloud images. Recently, graph convolutional network (GCN) is applied to provide the correlations for ground-based remote sensing cloud classification, in which the graph convolutional layer aggregates information from the connected nodes of graph in a weighted way. However, the weights assigned by GCN cannot reflect the importance of connected nodes precisely, which declines the discrimination of the aggregated features (AFs). To overcome the limitation, in this article, we propose the context graph attention network (CGAT) for ground-based remote sensing cloud classification. Specifically, the context graph attention layer (CGA layer) of CGAT is proposed to learn the context attention coefficients (CACs) and obtain the AFs of nodes based on the CACs. We compute the CACs not only considering the two connected nodes but also their neighborhood nodes in order to stabilize the aggregation process. In addition, we propose to utilize two different transformation matrices to transform the node and its connected nodes into new feature spaces, which could enhance the discrimination of AFs. We concatenate the AFs with the deep features (DFs) as final representations for cloud classification. Since existing ground-based cloud data sets (GCDs) have limited cloud images, we release a new data set named GCD that is the largest one for ground-based cloud classification. We conduct a series of experiments on GCD, and the experimental results verify the effectiveness of CGAT.

CloudA: A Ground-Based Cloud Classification Method with a Convolutional Neural Network

AbstractConventional classification methods are based on artificial experience to extract features, and each link is independent, which is a kind of “shallow learning.” As a result, the scope of the cloud category applied by this method is limited. In this paper, we propose a new convolutional neural network (CNN) with deep learning ability, called CloudA, for the ground-based cloud image recognition method. We use the Singapore Whole-Sky Imaging Categories (SWIMCAT) sample library and total-sky sample library to train and test CloudA. In particular, we visualize the cloud features captured by CloudA using the TensorBoard visualization method, and these features can help us to understand the process of ground-based cloud classification. We compare this method with other commonly used methods to explore the feasibility of using CloudA to classify ground-based cloud images, and the evaluation of a large number of experiments show that the average accuracy of this method is nearly 98.63% for ground-based cloud classification.

Deep tensor fusion network for multimodal ground-based cloud classification in weather station networks

Accurate multimodal ground-based cloud classification in weather station networks is a challenging task, because the existing methods fuse cloud visual data and multimodal data at the vector level resulting in the spatial information loss. In this work, we propose a method named deep tensor fusion network (DTFN) for multimodal ground-based cloud classification in weather station networks, which could learn completed cloud information by fusing heterogeneous features at the tensor level in a unified framework. The DTFN is composed of the visual tensor subnetwork (VTN) and the multimodal tensor subnetwork (MTN). The VTN transforms cloud images into cloud visual tensors using a deep network and therefore the spatial information of ground-based cloud images can be maintained. Meanwhile, the MTN is designed as a couple of deconvolutional layers in order to transform the multimodal data into multimodal tensors and ensure the multimodal tensors to be mathematically compatible with cloud visual tensors. Furthermore, to fuse cloud visual tensor and multimodal tensor, we propose the tensor fusion layer to exploit the high-order correlations between them. The DTFN is evaluated on MGCD and exceeds the state-of-the-art methods, which validates its effectiveness for multimodal ground-based cloud classification in weather station networks.

Hierarchical Multimodal Fusion for Ground-Based Cloud Classification in Weather Station Networks

Recently, the multimodal information is taken into consideration for ground-based cloud classification in weather station networks, but intrinsic correlations between the multimodal information and the visual information cannot be mined sufficiently. We propose a novel approach called hierarchical multimodal fusion (HMF) for ground-based cloud classification in weather station networks, which fuses the deep multimodal features and the deep visual features in different levels, i.e., low-level fusion and high-level fusion. The low-level fusion directly fuses the heterogeneous features, which focuses on the modality-specific fusion. The high-level fusion integrates the output of low-level fusion with deep visual features and deep multimodal features, which could learn complex correlations among them owing to the deep fusion structure. We employ one loss function to train the overall framework of the HMF so as to improve the discrimination of cloud representations. The experimental results on the MGCD dataset indicate that our method outperforms other methods, which verifies the effectiveness of the HMF in ground-based cloud classification.

Dual Guided Loss for Ground-Based Cloud Classification in Weather Station Networks

Dual Guided Loss for Ground-Based Cloud Classification in Weather Station Networks

Multimodal Ground-Based Cloud Classification Using Joint Fusion Convolutional Neural Network

The accurate ground-based cloud classification is a challenging task and still under development. The most current methods are limited to only taking the cloud visual features into consideration, which is not robust to the environmental factors. In this paper, we present the novel joint fusion convolutional neural network (JFCNN) to integrate the multimodal information for ground-based cloud classification. To learn the heterogeneous features (visual features and multimodal features) from the ground-based cloud data, we designed the proposed JFCNN as a two-stream structure which contains the vision subnetwork and multimodal subnetwork. We also proposed a novel layer named joint fusion layer to jointly learn two kinds of cloud features under one framework. After training the proposed JFCNN, we extracted the visual and multimodal features from the two subnetworks and integrated them using a weighted strategy. The proposed JFCNN was validated on the multimodal ground-based cloud (MGC) dataset and achieved remarkable performance, demonstrating its effectiveness for ground-based cloud classification task.

Ground-based cloud classification by learning stable local binary patterns

Feature selection and extraction is the first step in implementing pattern classification. The same is true for ground-based cloud classification. Histogram features based on local binary patterns (LBPs) are widely used to classify texture images. However, the conventional uniform LBP approach cannot capture all the dominant patterns in cloud texture images, thereby resulting in low classification performance. In this study, a robust feature extraction method by learning stable LBPs is proposed based on the averaged ranks of the occurrence frequencies of all rotation invariant patterns defined in the LBPs of cloud images. The proposed method is validated with a ground-based cloud classification database comprising five cloud types. Experimental results demonstrate that the proposed method achieves significantly higher classification accuracy than the uniform LBP, local texture patterns (LTP), dominant LBP (DLBP), completed LBP (CLTP) and salient LBP (SaLBP) methods in this cloud image database and under different noise conditions. And the performance of the proposed method is comparable with that of the popular deep convolutional neural network (DCNN) method, but with less computation complexity. Furthermore, the proposed method also achieves superior performance on an independent test data set.

Deep multimodal fusion for ground-based cloud classification in weather station networks

Most existing methods only utilize the visual sensors for ground-based cloud classification, which neglects other important characteristics of cloud. In this paper, we utilize the multimodal information collected from weather station networks for ground-based cloud classification and propose a novel method named deep multimodal fusion (DMF). In order to learn the visual features, we train a convolutional neural network (CNN) model to obtain the sum convolutional map (SCM) by using a pooling operation across all the feature maps in deep layers. Afterwards, we employ a weighted strategy to integrate the visual features with multimodal features. We validate the effectiveness of the proposed DMF on the multimodal ground-based cloud (MGC) dataset, and the experimental results demonstrate the proposed DMF achieves better results than the state-of-the-art methods.

Salient Dual Activations Aggregation for Ground-Based Cloud Classification in Weather Station Networks

Since appearances of clouds are always changeable, ground-based cloud classification is still in urgent need of development in weather station networks. Many existing methods resort to convolutional neural networks to improve the classification accuracy. However, these methods just carry out the feature extraction from one convolutional layer, hence making it difficult to obtain complete information of ground-based cloud images. To address this limitation, in this paper, we propose a novel method named salient dual activations aggregation (SDA2) to extract ground-based cloud features from different convolutional layers, which could learn the structural, textural, and high-level semantic information for ground-based cloud representation, simultaneously. Specifically, the salient patch selection strategy is first applied to select salient vectors from one shallow convolutional layer. Then, corresponding weights are learned from one deep convolutional layer. After obtaining a set of salient vectors with various weights, this paper is further designed to aggregate them into a representative vector for each ground-based cloud image by explicitly modeling the relationship among salient vectors. The proposed SDA2 is validated on three ground-based cloud databases, and the experimental results prove its effectiveness. Especially, we obtain the promising classification results of 91.24% on the MOC_e database, 91.15% on the IAP_e database, and 88.73% on the CAMS_e database.

Cross-Domain Ground-Based Cloud Classification Based on Transfer of Local Features and Discriminative Metric Learning

Cross-domain ground-based cloud classification is a challenging issue as the appearance of cloud images from different cloud databases possesses extreme variations. Two fundamental problems which are essential for cross-domain ground-based cloud classification are feature representation and similarity measurement. In this paper, we propose an effective feature representation called transfer of local features (TLF), and measurement method called discriminative metric learning (DML). The TLF is a generalized representation framework that can integrate various kinds of local features, e.g., local binary patterns (LBP), local ternary patterns (LTP) and completed LBP (CLBP). In order to handle domain shift, such as variations of illumination, image resolution, capturing location, occlusion and so on, the TLF mines the maximum response in regions to make a stable representation for domain variations. We also propose to learn a discriminant metric, simultaneously. We make use of sample pairs and the relationship among cloud classes to learn the distance metric. Furthermore, in order to improve the practicability of the proposed method, we replace the original cloud images with the convolutional activation maps which are then applied to TLF and DML. The proposed method has been validated on three cloud databases which are collected in China alone, provided by Chinese Academy of Meteorological Sciences (CAMS), Meteorological Observation Centre (MOC), and Institute of Atmospheric Physics (IAP). The classification accuracies outperform the state-of-the-art methods.

Deep Convolutional Activations-Based Features for Ground-Based Cloud Classification

Ground-based cloud classification is crucial for meteorological research and has received great concern in recent years. However, it is very challenging due to the extreme appearance variations under different atmospheric conditions. Although the convolutional neural networks have achieved remarkable performance in image classification, no one has evaluated their suitability for cloud classification. In this letter, we propose to use the deep convolutional activations-based features (DCAFs) for ground-based cloud classification. Considering the unique characteristic of cloud, we believe the local rich texture information might be more important than the global layout information and, thus, give a comprehensive evaluation of using both shallow convolutional layers-based features and DCAFs. Experimental results on two challenging public data sets demonstrate that although the realization of DCAF is quite straightforward without any use-dependent tricks, it outperforms conventional hand-crafted features considerably.

Learning group patterns for ground-based cloud classification in wireless sensor networks

Cloud classification of ground-based images is a challenging task due to extreme variations under different atmospheric conditions. With the development of wireless sensor networks (WSN), it provides the possibility to understand and classify clouds more accurately. Recent research has focused on extracting discriminative cloud image features in WSN, which plays a crucial role in achieving competitive classification performance. In this paper, a novel feature extraction algorithm by learning group patterns in WSN is proposed for ground-based cloud classification. The proposed descriptors take texture resolution variations into account by cascading the salient local binary pattern (SLBP) information of hierarchical spatial pyramids. Through learning group patterns, we can obtain more useful information for cloud representation in WSN. Experimental results using ground-based cloud databases demonstrate that the proposed method can achieve better results than the current methods.

Ground-based cloud classification using weighted local binary patterns

Ground-based cloud classification plays an essential role in meteorological research, and, recently, texture classification techniques have been introduced to automate the process. As a typical texture descriptor, local binary patterns (LBP) have emerged as a very powerful tool due to their effective representation ability. However, it neglects the local contrast information of ground-based cloud images, which may hinder the classification performance. We propose a descriptor called weighted local binary patterns (WLBP) for ground-based cloud classification. The proposed WLBP not only inherits the advantages of LBP but also encodes the useful contrast information of local structures. We define the variance of a local patch as a rotation invariant measure and use this measure as an adaptive weight to adjust the contribution of each neighboring pixel in the process of histogram accumulation. The experimental results demonstrate that the proposed WLBP achieves a better performance than the state-of-the-art methods.

Information integration for ground-based cloud classification using joint consistent sparse coding in heterogeneous sensor network

Although sparsity-based algorithm has emerged as an extremely powerful tool for information integration, it neglects the relationship of heterogeneous features and coding coefficients from the same class in the training stage, which may cause declining of the classification performance. In this paper, we focus on information integration for ground-based cloud classification in heterogeneous sensor network (HSN), and propose a novel coding strategy named joint consistent sparse coding (JCSC) to overcome the drawbacks of traditional sparse coding. In order to integrate information effectively, we add a joint sparse regularization to learn the relationship of heterogeneous features. Moreover, we utilize the consistent constraint on coding coefficients. In this way, coding coefficients from the same class can be forced to their mean vector, and therefore they are more compact and discriminative. The experimental results demonstrate that our method achieves better performance than the state-of-the-art methods.

Learning Discriminative Features for Ground-Based Cloud Classification via Mutual Information Maximization

Texture feature descriptors such as local binary patterns (LBP) have proven effective for ground-based cloud classification. Traditionally, these texture feature descriptors are predefined in a handcrafted way. In this paper, we propose a novel method which automatically learns discriminative features from labeled samples for ground-based cloud classification. Our key idea is to learn these features through mutual information maximization which learns a transformation matrix for local difference vectors of LBP. The experimental results show that our learned features greatly improves the performance of ground-based cloud classification when compared to the other state-of-the-art methods.

Learning Discriminative Salient LBP for Cloud Classification in Wireless Sensor Networks

We focus on the issue of ground-based cloud classification in wireless sensor networks (WSN) and propose a novel feature learning algorithm named discriminative salient local binary pattern (DSLBP) to tackle this issue. The proposed method is a two-layer model for learning discriminative patterns. The first layer is designed to learn the most salient and robust patterns from each class, and the second layer is used to obtain features with discriminative power and representation capability. Based on this strategy, discriminative patterns are obtained according to the characteristics of training cloud data from different sensor nodes, which can adapt variant cloud images. The experimental results show that the proposed algorithm achieves better results than other state-of-the-art cloud classification algorithms in WSN.

Salient local binary pattern for ground-based cloud classification

Ground-based cloud classification is challenging due to extreme variations in the appearance of clouds under different atmospheric conditions. Texture classification techniques have recently been introduced to deal with this issue. A novel texture descriptor, the salient local binary pattern (SLBP), is proposed for ground-based cloud classification. The SLBP takes advantage of the most frequently occurring patterns (the salient patterns) to capture descriptive information. This feature makes the SLBP robust to noise. Experimental results using ground-based cloud images demonstrate that the proposed method can achieve better results than current state-of-the-art methods.