The Experts below are selected from a list of 592746 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: Biao Hou, 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.

Biao Hou - 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: Biao Hou, 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.

Sos Agaian - One of the best experts on this subject based on the ideXlab platform.

  • wavelet transform coefficient histogram based image enhancement algorithms
    Proceedings of SPIE, 2010
    Co-Authors: Junjun Xia, Karen Panetta, Sos Agaian
    Abstract:

    This paper proposes two image enhancement algorithms that are based on utilizing histogram data gathered from wavelet transform domain coefficients. Computer simulations demonstrate that combining the Spatial Method of histogram equalization with the logarithmic transform domain coefficient histograms achieves a much more balanced enhancement, which outperforms classical histogram equalization algorithms.

  • Image enhancement based on transform coefficient histogram shifting and shaping
    2010 IEEE International Conference on Technologies for Homeland Security (HST), 2010
    Co-Authors: Karen Panetta, Sos Agaian
    Abstract:

    This paper proposes two image enhancement algorithms that are based on utilizing histogram data gathered from wavelet transform domain coefficients. Computer simulations demonstrate that combining the Spatial Method of histogram equalization with the wavelet transform domain coefficient histograms achieves a much more balanced enhancement, which outperforms classical histogram equalization algorithms.

  • logarithmic transform coefficient histogram matching with Spatial equalization
    Visual Information Processing Conference, 2005
    Co-Authors: B Silver, Sos Agaian, Karen Panetta
    Abstract:

    In this paper we propose an image enhancement algorithm that is based on utilizing histogram data gathered from transform domain coefficients that will improve on the limitations of the histogram equalization Method. Traditionally, classical histogram equalization has had some problems due to its inherent dynamic range expansion. Many images with data tightly clustered around certain intensity values can be over enhanced by standard histogram equalization, leading to artifacts and overall tonal change of the image. In the transform domain, one has control over subtle image properties such as low and high frequency content with their respective magnitudes and phases. However, due to the nature of many of these transforms, the coefficient’s histograms may be so tightly packed that distinguishing them from one another may be impossible. By placing the transform coefficients in the logarithmic transform domain, it is easy to see the difference between different quality levels of images based upon their logarithmic transform coefficient histograms. Our results demonstrate that combing the Spatial Method of histogram equalization with logarithmic transform domain coefficient histograms achieves a much more balanced enhancement, that out performs classical histogram equalization.

Karen Panetta - One of the best experts on this subject based on the ideXlab platform.

  • wavelet transform coefficient histogram based image enhancement algorithms
    Proceedings of SPIE, 2010
    Co-Authors: Junjun Xia, Karen Panetta, Sos Agaian
    Abstract:

    This paper proposes two image enhancement algorithms that are based on utilizing histogram data gathered from wavelet transform domain coefficients. Computer simulations demonstrate that combining the Spatial Method of histogram equalization with the logarithmic transform domain coefficient histograms achieves a much more balanced enhancement, which outperforms classical histogram equalization algorithms.

  • Image enhancement based on transform coefficient histogram shifting and shaping
    2010 IEEE International Conference on Technologies for Homeland Security (HST), 2010
    Co-Authors: Karen Panetta, Sos Agaian
    Abstract:

    This paper proposes two image enhancement algorithms that are based on utilizing histogram data gathered from wavelet transform domain coefficients. Computer simulations demonstrate that combining the Spatial Method of histogram equalization with the wavelet transform domain coefficient histograms achieves a much more balanced enhancement, which outperforms classical histogram equalization algorithms.

  • logarithmic transform coefficient histogram matching with Spatial equalization
    Visual Information Processing Conference, 2005
    Co-Authors: B Silver, Sos Agaian, Karen Panetta
    Abstract:

    In this paper we propose an image enhancement algorithm that is based on utilizing histogram data gathered from transform domain coefficients that will improve on the limitations of the histogram equalization Method. Traditionally, classical histogram equalization has had some problems due to its inherent dynamic range expansion. Many images with data tightly clustered around certain intensity values can be over enhanced by standard histogram equalization, leading to artifacts and overall tonal change of the image. In the transform domain, one has control over subtle image properties such as low and high frequency content with their respective magnitudes and phases. However, due to the nature of many of these transforms, the coefficient’s histograms may be so tightly packed that distinguishing them from one another may be impossible. By placing the transform coefficients in the logarithmic transform domain, it is easy to see the difference between different quality levels of images based upon their logarithmic transform coefficient histograms. Our results demonstrate that combing the Spatial Method of histogram equalization with logarithmic transform domain coefficient histograms achieves a much more balanced enhancement, that out performs classical histogram equalization.

S Maci - One of the best experts on this subject based on the ideXlab platform.

  • a hybrid spectral Spatial Method to evaluate the active green s function of large planar rectangular arrays a combined asymptotic numerical algorithm
    IEEE Transactions on Antennas and Propagation, 2006
    Co-Authors: F Mariottini, A Cucini, S Maci
    Abstract:

    The article illustrates a formulation to evaluate the array Green's function (AGF) of large finite planar phased array for observation points on the array plane, possibly close to the array contour. The procedure is based on the AGF representation in terms of a double spectral integral, whose integration paths are properly deformed to have an exponential attenuation of the integrand. The diffraction integral is evaluated numerically for point close to the array edges while an asymptotic treatment is proposed far from the edges. This latter comprises higher order contributions. Thanks to the convergence properties, the final algorithm is numerically accurate, stable and more efficient with respect to the individual element summation for large arrays. It also constitutes the basic step for the efficient evaluation of the AGF of a multilayer environment