The Experts below are selected from a list of 2301 Experts worldwide ranked by ideXlab platform
Zhong Zhang - One of the best experts on this subject based on the ideXlab platform.
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Deep tensor fusion network for multimodal ground-based Cloud Classification in weather station networks
Ad Hoc Networks, 2020Co-Authors: Mei Li, Zhong ZhangAbstract:Abstract 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.
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Learning discriminative salient LBP for Cloud Classification in wireless sensor networks
International Journal of Distributed Sensor Networks, 2020Co-Authors: Zhong ZhangAbstract: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.
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Evaluation Embedding Features for Ground-Based Cloud Classification
Communications Signal Processing and Systems, 2020Co-Authors: Zhong Zhang, Donghong Li, Shuang LiuAbstract:Ground-based Cloud Classification plays a vital important role in meteorological research. However, the existing methods perform well confined to one weather station. In this paper, we present a detailed introduction of two representative embedding features for ground-based Cloud Classification in various weather stations. The features are learned from the metric learning and the convolutional neural network (CNN), respectively. The two kinds of features are evaluated on two weather stations.
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Evaluation Embedding Features for Ground-Based Cloud Classification
Lecture Notes in Electrical Engineering, 2019Co-Authors: Zhong Zhang, Donghong LiAbstract:Ground-based Cloud Classification plays a vital important role in meteorological research. However, the existing methods perform well confined to one weather station. In this paper, we present a detailed introduction of two representative embedding features for ground-based Cloud Classification in various weather stations. The features are learned from the metric learning and the convolutional neural network (CNN), respectively. The two kinds of features are evaluated on two weather stations.
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Hierarchical Multimodal Fusion for Ground-Based Cloud Classification in Weather Station Networks
IEEE Access, 2019Co-Authors: Linlin Duan, Zhong ZhangAbstract: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.
Baihua Xiao - One of the best experts on this subject based on the ideXlab platform.
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Joint Encoding LBP Features from Infrared and Visible-Light Cloud Image Observations for Ground-Based Cloud Classification
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018Co-Authors: Yu Wang, Chunheng Wang, Baihua XiaoAbstract:Cloud type Classification based on ground-based Cloud image observations is an important task in atmospheric research. Currently, two kinds of Cloud image observations with infrared and visible light images are widely used for Cloud Classification. However, they are only independently analyzed and simply compared in the current study. The useful information from these two kinds of images is not fully utilized and integrated. The Classification performance could be improved if taking full advantage of the complementary information of these two observations. Thus, first, a database containing these two kinds of Cloud images with same temporal resolution is released in this study. Then, a two-observation joint encoding strategy of LBP (local binary pattern) features is proposed to implement Cloud Classification by encoding the joint distribution of LBP patterns in different observations, which captures the correlation between two observations. Experimental results based on this database show the significant superiority of the proposed method compared to the results based on the single observation.
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ground based Cloud Classification by learning stable local binary patterns
Atmospheric Research, 2018Co-Authors: Chunheng Wang, Yu Wang, Baihua XiaoAbstract:Abstract 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.
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Multimodal Ground-Based Cloud Classification Using Joint Fusion Convolutional Neural Network
Remote Sensing, 2018Co-Authors: Mei Li, Zhong Zhang, Baihua XiaoAbstract: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.
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Deep Convolutional Activations-Based Features for Ground-Based Cloud Classification
IEEE Geoscience and Remote Sensing Letters, 2017Co-Authors: Chunheng Wang, Yu Wang, Baihua XiaoAbstract: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.
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Learning Discriminative Features for Ground-Based Cloud Classification via Mutual Information Maximization
IEICE Transactions on Information and Systems, 2015Co-Authors: Zhong Zhang, Baihua XiaoAbstract: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.
Yu Wang - One of the best experts on this subject based on the ideXlab platform.
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Joint Encoding LBP Features from Infrared and Visible-Light Cloud Image Observations for Ground-Based Cloud Classification
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018Co-Authors: Yu Wang, Chunheng Wang, Baihua XiaoAbstract:Cloud type Classification based on ground-based Cloud image observations is an important task in atmospheric research. Currently, two kinds of Cloud image observations with infrared and visible light images are widely used for Cloud Classification. However, they are only independently analyzed and simply compared in the current study. The useful information from these two kinds of images is not fully utilized and integrated. The Classification performance could be improved if taking full advantage of the complementary information of these two observations. Thus, first, a database containing these two kinds of Cloud images with same temporal resolution is released in this study. Then, a two-observation joint encoding strategy of LBP (local binary pattern) features is proposed to implement Cloud Classification by encoding the joint distribution of LBP patterns in different observations, which captures the correlation between two observations. Experimental results based on this database show the significant superiority of the proposed method compared to the results based on the single observation.
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ground based Cloud Classification by learning stable local binary patterns
Atmospheric Research, 2018Co-Authors: Chunheng Wang, Yu Wang, Baihua XiaoAbstract:Abstract 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.
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Deep Convolutional Activations-Based Features for Ground-Based Cloud Classification
IEEE Geoscience and Remote Sensing Letters, 2017Co-Authors: Chunheng Wang, Yu Wang, Baihua XiaoAbstract: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.
Chunheng Wang - One of the best experts on this subject based on the ideXlab platform.
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Joint Encoding LBP Features from Infrared and Visible-Light Cloud Image Observations for Ground-Based Cloud Classification
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018Co-Authors: Yu Wang, Chunheng Wang, Baihua XiaoAbstract:Cloud type Classification based on ground-based Cloud image observations is an important task in atmospheric research. Currently, two kinds of Cloud image observations with infrared and visible light images are widely used for Cloud Classification. However, they are only independently analyzed and simply compared in the current study. The useful information from these two kinds of images is not fully utilized and integrated. The Classification performance could be improved if taking full advantage of the complementary information of these two observations. Thus, first, a database containing these two kinds of Cloud images with same temporal resolution is released in this study. Then, a two-observation joint encoding strategy of LBP (local binary pattern) features is proposed to implement Cloud Classification by encoding the joint distribution of LBP patterns in different observations, which captures the correlation between two observations. Experimental results based on this database show the significant superiority of the proposed method compared to the results based on the single observation.
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ground based Cloud Classification by learning stable local binary patterns
Atmospheric Research, 2018Co-Authors: Chunheng Wang, Yu Wang, Baihua XiaoAbstract:Abstract 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.
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Deep Convolutional Activations-Based Features for Ground-Based Cloud Classification
IEEE Geoscience and Remote Sensing Letters, 2017Co-Authors: Chunheng Wang, Yu Wang, Baihua XiaoAbstract: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.
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salient local binary pattern for ground based Cloud Classification
Acta Meteorologica Sinica, 2013Co-Authors: Chunheng Wang, Zhong Zhang, Baihua Xiao, Yunxue ShaoAbstract: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.
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Illumination-invariant completed LTP descriptor for Cloud Classification
2012 5th International Congress on Image and Signal Processing, 2012Co-Authors: Chunheng Wang, Zhong Zhang, Baihua Xiao, Yunxue ShaoAbstract:Cloud Classification plays an essential role in a large number of applications. However, this issue is particularly challenging due to the extreme appearance variations under different atmospheric conditions. In this paper, a novel descriptor named illumination-invariant completed local ternary pattern (ICLTP) is proposed for Cloud Classification. The proposed descriptor effectively handles the illumination variations by introducing illumination invariant factor. Furthermore, the Quadratic-Chi metric, which is more suitable for comparing the difference between two histograms, is applied instead of Chi-Square metric. The experimental results demonstrate the superior performance of our strategy on two challenging Cloud databases. Besides Cloud Classification, we further validate the proposed ICLTP operator on traditional texture Classification, which show the good generalization ability.
Mei Li - One of the best experts on this subject based on the ideXlab platform.
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Deep tensor fusion network for multimodal ground-based Cloud Classification in weather station networks
Ad Hoc Networks, 2020Co-Authors: Mei Li, Zhong ZhangAbstract:Abstract 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.
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Multimodal GAN for Energy Efficiency and Cloud Classification in Internet of Things
IEEE Internet of Things Journal, 2019Co-Authors: Mei LiAbstract:Efficient processing of large-scale multimodal sensor data is a key issue for applying the Internet of Things (IoT). Accurate Cloud Classification is critical for weather and climate monitoring, which are parts of IoT applications. In this paper, we propose a novel generative deep model named multimodal generative adversarial network (Multimodal GAN) to improve both the energy efficiency and the Cloud Classification accuracy in IoT. The proposed Multimodal GAN is composed of a discriminator and a generator, each of which is devised to a two-stream network. The branches of two-stream structure correspond to the Cloud visual information and the Cloud scalar information, respectively. Therefore, the Multimodal GAN is capable of generating the Cloud visual information and Cloud scalar information simultaneously. Afterward, the training set is extended by the generated multimodal Cloud samples, and the deep multimodal Cloud Classification model is trained by the extended training set. As a result, the Classification model possesses high generalization ability and is less prone to be over-fitting. Moreover, the feature representations extracted from the Classification model reflect the salient information of raw multimodal Cloud data, and therefore they can be stored and transmitted in IoT. The effectiveness of the proposed method in energy efficiency and Cloud Classification is validated on the multimodal Cloud dataset.
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Multimodal Ground-Based Cloud Classification Using Joint Fusion Convolutional Neural Network
Remote Sensing, 2018Co-Authors: Mei Li, Zhong Zhang, Baihua XiaoAbstract: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.
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Deep multimodal fusion for ground-based Cloud Classification in weather station networks
Eurasip Journal on Wireless Communications and Networking, 2018Co-Authors: Mei LiAbstract: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.