Enhancing dust storm detection using PCA based data fusion

Agarwal, Alekh; El‐Ghazawi, Tarek; Kafatos, Menas; LeMoigne, Jacqueline

doi:10.1109/igarss.2005.1525391

“…Also in [9] it is reported a combination of information obtained from the sensors MODIS and TOMS. Finally, the MISR sensor is used in [15]. With regard to the type of image processing techniques used, the information is the following.…”

Section: Literature Reviewmentioning

confidence: 99%

Dust Storm Detection Using a Neural Network with Uncertainty and Ambiguity Output Analysis

Chacón-Murguía¹,

Quezada-Holguín²,

Rivas

³

et al. 2011

Lecture Notes in Computer Science

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Dust storms are meteorological phenomena that may affect human life. Therefore, it is of great interest to work towards the development of a stand-alone dust storm detection system that may help to prevent and/or counteract its negative effects. This work proposes a dust storm detection system based on an Artificial Neural Network, ANN. The ANN is designed to identify not just dust storm areas but also vegetation and soil. The proposed ANN works on information obtained from multispectral images acquired with the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. Before the multispectral information is fed to the ANN a process to remove cloud regions from images is performed in order to reduce the computational burden. A method to manage undefined and ambiguous ANN outputs is also proposed in the paper which significantly reduces the false positives rate. Results of this research present a suitable performance at detecting the dust storm events.

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“…In order to create a subset of new features by a combination of the original features, some linear strategies are typically used to discard redundant components and reduce high dimensionality of the data. These linear methods consist of principal component analysis (PCA) [73], linear discriminant analysis (LDA) [74], canonical correlation analysis (CCA) [75], cross-modal factor analysis (CFA) [76], etc.…”

Section: Introduction Of Feature Level Fusionmentioning

confidence: 99%

Audiovisual Emotion Recognition Using Entropy-estimation-based Multimodal Information Fusion

Xie¹

2021

Preprint

0

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Understanding human emotional states is indispensable for our daily interaction, and we can enjoy more natural and friendly human computer interaction (HCI) experience by fully utilizing human’s affective states. In the application of emotion recognition, multimodal information fusion is widely used to discover the relationships of multiple information sources and make joint use of a number of channels, such as speech, facial expression, gesture and physiological processes. This thesis proposes a new framework of emotion recognition using information fusion based on the estimation of information entropy. The novel techniques of information theoretic learning are applied to feature level fusion and score level fusion. The most critical issues for feature level fusion are feature transformation and dimensionality reduction. The existing methods depend on the second order statistics, which is only optimal for Gaussian-like distributions. By incorporating information theoretic tools, a new feature level fusion method based on kernel entropy component analysis is proposed. For score level fusion, most previous methods focus on predefined rule based approaches, which are usually heuristic. In this thesis, a connection between information fusion and maximum correntropy criterion is established for effective score level fusion. Feature level fusion and score level fusion methods are then combined to introduce a two-stage fusion platform. The proposed methods are applied to audiovisual emotion recognition, and their effectiveness is evaluated by experiments on two publicly available audiovisual emotion databases. The experimental results demonstrate that the proposed algorithms achieve improved performance in comparison with the existing methods. The work of this thesis offers a promising direction to design more advanced emotion recognition systems based on multimodal information fusion and has great significance to the development of intelligent human computer interaction systems.

show abstract

“…In order to create a subset of new features by a combination of the original features, some linear strategies are typically used to discard redundant components and reduce high dimensionality of the data. These linear methods consist of principal component analysis (PCA) [73], linear discriminant analysis (LDA) [74], canonical correlation analysis (CCA) [75], cross-modal factor analysis (CFA) [76], etc.…”

Section: Introduction Of Feature Level Fusionmentioning

confidence: 99%

Audiovisual Emotion Recognition Using Entropy-estimation-based Multimodal Information Fusion

Xie¹

2021

Preprint

0

View full text Add to dashboard Cite

Understanding human emotional states is indispensable for our daily interaction, and we can enjoy more natural and friendly human computer interaction (HCI) experience by fully utilizing human’s affective states. In the application of emotion recognition, multimodal information fusion is widely used to discover the relationships of multiple information sources and make joint use of a number of channels, such as speech, facial expression, gesture and physiological processes. This thesis proposes a new framework of emotion recognition using information fusion based on the estimation of information entropy. The novel techniques of information theoretic learning are applied to feature level fusion and score level fusion. The most critical issues for feature level fusion are feature transformation and dimensionality reduction. The existing methods depend on the second order statistics, which is only optimal for Gaussian-like distributions. By incorporating information theoretic tools, a new feature level fusion method based on kernel entropy component analysis is proposed. For score level fusion, most previous methods focus on predefined rule based approaches, which are usually heuristic. In this thesis, a connection between information fusion and maximum correntropy criterion is established for effective score level fusion. Feature level fusion and score level fusion methods are then combined to introduce a two-stage fusion platform. The proposed methods are applied to audiovisual emotion recognition, and their effectiveness is evaluated by experiments on two publicly available audiovisual emotion databases. The experimental results demonstrate that the proposed algorithms achieve improved performance in comparison with the existing methods. The work of this thesis offers a promising direction to design more advanced emotion recognition systems based on multimodal information fusion and has great significance to the development of intelligent human computer interaction systems.

show abstract

Enhancing dust storm detection using PCA based data fusion

Cited by 7 publications

References 13 publications

Dust Storm Detection Using a Neural Network with Uncertainty and Ambiguity Output Analysis

Dust Storm Detection Using a Neural Network with Uncertainty and Ambiguity Output Analysis

Audiovisual Emotion Recognition Using Entropy-estimation-based Multimodal Information Fusion

Audiovisual Emotion Recognition Using Entropy-estimation-based Multimodal Information Fusion

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