The Experts below are selected from a list of 24375 Experts worldwide ranked by ideXlab platform

Licheng Jiao - One of the best experts on this subject based on the ideXlab platform.

  • Spectral–Spatial Classification of Hyperspectral Data Using 3-D Morphological Profile
    IEEE Geoscience and Remote Sensing Letters, 2015
    Co-Authors: Taimin Huang, Licheng Jiao
    Abstract:

    A new spectral-spatial method based on a 3-D morphological profile (3D-MP) is proposed for Hyperspectral Data classification. As an extension of a previous approach, the proposed method uses both the spectral and spatial information for classification. First, random projection (RP) is used for dimensionality reduction of Hyperspectral Data. After RP in spectral domain, a novel 3D-MP method is proposed to exploit the dependence between Data. Finally, the classification is performed by the widely used support vector machine classifier. Our experiments reveal that the proposed approach exploits the 3-D spectral-spatial feature to provide the state-of-the-art classification results for different Hyperspectral Data sets.

Xing Zhao - One of the best experts on this subject based on the ideXlab platform.

  • spectral spatial classification of Hyperspectral Data based on deep belief network
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015
    Co-Authors: Yushi Chen, Xing Zhao
    Abstract:

    Hyperspectral Data classification is a hot topic in remote sensing community. In recent years, significant effort has been focused on this issue. However, most of the methods extract the features of original Data in a shallow manner. In this paper, we introduce a deep learning approach into Hyperspectral image classification. A new feature extraction (FE) and image classification framework are proposed for Hyperspectral Data analysis based on deep belief network (DBN). First, we verify the eligibility of restricted Boltzmann machine (RBM) and DBN by the following spectral information-based classification. Then, we propose a novel deep architecture, which combines the spectral–spatial FE and classification together to get high classification accuracy. The framework is a hybrid of principal component analysis (PCA), hierarchical learning-based FE, and logistic regression (LR). Experimental results with Hyperspectral Data indicate that the classifier provide competitive solution with the state-of-the-art methods. In addition, this paper reveals that deep learning system has huge potential for Hyperspectral Data classification.

  • Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015
    Co-Authors: Yushi Chen, Xing Zhao
    Abstract:

    Hyperspectral Data classification is a hot topic in remote sensing community. In recent years, significant effort has been focused on this issue. However, most of the methods extract the features of original Data in a shallow manner. In this paper, we introduce a deep learning approach into Hyperspectral image classification. A new feature extraction (FE) and image classification framework are proposed for Hyperspectral Data analysis based on deep belief network (DBN). First, we verify the eligibility of restricted Boltzmann machine (RBM) and DBN by the following spectral information-based classification. Then, we propose a novel deep architecture, which combines the spectral-spatial FE and classification together to get high classification accuracy. The framework is a hybrid of principal component analysis (PCA), hierarchical learning-based FE, and logistic regression (LR). Experimental results with Hyperspectral Data indicate that the classifier provide competitive solution with the state-of-the-art methods. In addition, this paper reveals that deep learning system has huge potential for Hyperspectral Data classification.

  • Deep learning-based classification of Hyperspectral Data
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014
    Co-Authors: Yushi Chen, Zhouhan Lin, Xing Zhao, Gang Wang, Yanfeng Gu
    Abstract:

    Classification is one of the most popular topics in Hyperspectral remote sensing. In the last two decades, a huge number of methods were proposed to deal with the Hyperspectral Data classification problem. However, most of them do not hierarchically extract deep features. In this paper, the concept of deep learning is introduced into Hyperspectral Data classification for the first time. First, we verify the eligibility of stacked autoencoders by following classical spectral information-based classification. Second, a new way of classifying with spatial-dominated information is proposed. We then propose a novel deep learning framework to merge the two features, from which we can get the highest classification accuracy. The framework is a hybrid of principle component analysis (PCA), deep learning architecture, and logistic regression. Specifically, as a deep learning architecture, stacked autoencoders are aimed to get useful high-level features. Experimental results with widely-used Hyperspectral Data indicate that classifiers built in this deep learning-based framework provide competitive performance. In addition, the proposed joint spectral-spatial deep neural network opens a new window for future research, showcasing the deep learning-based methods' huge potential for accurate Hyperspectral Data classification.

Taimin Huang - One of the best experts on this subject based on the ideXlab platform.

  • Spectral–Spatial Classification of Hyperspectral Data Using 3-D Morphological Profile
    IEEE Geoscience and Remote Sensing Letters, 2015
    Co-Authors: Taimin Huang, Licheng Jiao
    Abstract:

    A new spectral-spatial method based on a 3-D morphological profile (3D-MP) is proposed for Hyperspectral Data classification. As an extension of a previous approach, the proposed method uses both the spectral and spatial information for classification. First, random projection (RP) is used for dimensionality reduction of Hyperspectral Data. After RP in spectral domain, a novel 3D-MP method is proposed to exploit the dependence between Data. Finally, the classification is performed by the widely used support vector machine classifier. Our experiments reveal that the proposed approach exploits the 3-D spectral-spatial feature to provide the state-of-the-art classification results for different Hyperspectral Data sets.

Yushi Chen - One of the best experts on this subject based on the ideXlab platform.

  • Hyperspectral Data clustering based on density analysis ensemble
    Remote Sensing Letters, 2016
    Co-Authors: Yushi Chen, Shunli Ma, Xi Chen, Pedram Ghamisi
    Abstract:

    ABSTRACTIn this letter, we present a new Hyperspectral Data-clustering method, named density analysis ensemble, from a different perspective. Instead of distance-based metrics in traditional clustering methods, we use density analysis for Hyperspectral Data clustering. Moreover, in order to improve the performance, we use the random subspace ensemble method to formulate a set of clustering systems. The final results are retrieved through majority voting. Compared to the k-means method, the overall accuracies have been improved by 7.05% and 6.93% for the Salinas and Pavia University Data sets, respectively.

  • spectral spatial classification of Hyperspectral Data based on deep belief network
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015
    Co-Authors: Yushi Chen, Xing Zhao
    Abstract:

    Hyperspectral Data classification is a hot topic in remote sensing community. In recent years, significant effort has been focused on this issue. However, most of the methods extract the features of original Data in a shallow manner. In this paper, we introduce a deep learning approach into Hyperspectral image classification. A new feature extraction (FE) and image classification framework are proposed for Hyperspectral Data analysis based on deep belief network (DBN). First, we verify the eligibility of restricted Boltzmann machine (RBM) and DBN by the following spectral information-based classification. Then, we propose a novel deep architecture, which combines the spectral–spatial FE and classification together to get high classification accuracy. The framework is a hybrid of principal component analysis (PCA), hierarchical learning-based FE, and logistic regression (LR). Experimental results with Hyperspectral Data indicate that the classifier provide competitive solution with the state-of-the-art methods. In addition, this paper reveals that deep learning system has huge potential for Hyperspectral Data classification.

  • Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015
    Co-Authors: Yushi Chen, Xing Zhao
    Abstract:

    Hyperspectral Data classification is a hot topic in remote sensing community. In recent years, significant effort has been focused on this issue. However, most of the methods extract the features of original Data in a shallow manner. In this paper, we introduce a deep learning approach into Hyperspectral image classification. A new feature extraction (FE) and image classification framework are proposed for Hyperspectral Data analysis based on deep belief network (DBN). First, we verify the eligibility of restricted Boltzmann machine (RBM) and DBN by the following spectral information-based classification. Then, we propose a novel deep architecture, which combines the spectral-spatial FE and classification together to get high classification accuracy. The framework is a hybrid of principal component analysis (PCA), hierarchical learning-based FE, and logistic regression (LR). Experimental results with Hyperspectral Data indicate that the classifier provide competitive solution with the state-of-the-art methods. In addition, this paper reveals that deep learning system has huge potential for Hyperspectral Data classification.

  • Deep learning-based classification of Hyperspectral Data
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014
    Co-Authors: Yushi Chen, Zhouhan Lin, Xing Zhao, Gang Wang, Yanfeng Gu
    Abstract:

    Classification is one of the most popular topics in Hyperspectral remote sensing. In the last two decades, a huge number of methods were proposed to deal with the Hyperspectral Data classification problem. However, most of them do not hierarchically extract deep features. In this paper, the concept of deep learning is introduced into Hyperspectral Data classification for the first time. First, we verify the eligibility of stacked autoencoders by following classical spectral information-based classification. Second, a new way of classifying with spatial-dominated information is proposed. We then propose a novel deep learning framework to merge the two features, from which we can get the highest classification accuracy. The framework is a hybrid of principle component analysis (PCA), deep learning architecture, and logistic regression. Specifically, as a deep learning architecture, stacked autoencoders are aimed to get useful high-level features. Experimental results with widely-used Hyperspectral Data indicate that classifiers built in this deep learning-based framework provide competitive performance. In addition, the proposed joint spectral-spatial deep neural network opens a new window for future research, showcasing the deep learning-based methods' huge potential for accurate Hyperspectral Data classification.

Klausrobert Muller - One of the best experts on this subject based on the ideXlab platform.

  • unmixing Hyperspectral Data
    Neural Information Processing Systems, 1999
    Co-Authors: Lucas C Parra, Clay D Spence, Paul Sajda, Andreas Ziehe, Klausrobert Muller
    Abstract:

    In Hyperspectral imagery one pixel typically consists of a mixture of the reflectance spectra of several materials, where the mixture coefficients correspond to the abundances of the constituting materials. We assume linear combinations of reflectance spectra with some additive normal sensor noise and derive a probabilistic MAP framework for analyzing Hyperspectral Data. As the material reflectance characteristics are not know a priori, we face the problem of unsupervised linear unmixing. The incorporation of different prior information (e.g. positivity and normalization of the abundances) naturally leads to a family of interesting algorithms, for example in the noise-free case yielding an algorithm that can be understood as constrained independent component analysis (ICA). Simulations underline the usefulness of our theory.