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Browsing by Author "Lunga, D"

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  • Lunga, D (PRASA 2013 Proceedings, 2013-12)
    Over the past decade, the problem of hyperspectral data clustering has generated a growing interest from various fields including the machine learning community. This paper presents an analysis of the traditional spectral ...
  • Lunga, D; Ward, S; Msimango, N (Pattern Recognition Association of South Africa, 2014-11)
    Interactive data visualization is a transformational technique that can turn raw data into immersive insights extraction. In this study, an interactive dashboard that visualizes raw aggregated data is developed to identify ...
  • Lunga, D; Ersoy, O (IEEE, 2011-10)
    Modern remote sensing imaging sensor technology provides detailed spectral and spatial information that enables precise analysis of land cover usage. From a research point of view, traditional widely used statistical models ...
  • Lunga, D; Prasad, S; Crawford, M; Ersoy, O (IEEE Xplore, 2013-08)
    Advances in hyperspectral sensing provide new capability for characterizing spectral signatures in a wide range of physical and biological systems, while inspiring new methods for extracting information from these data. ...
  • Lunga, D; Prasad, S; Crawford, M; Ersoy, O (IEEE, 2014-01)
    Interest in manifold learning for representing the topology of large, high dimensional nonlinear data sets in lower, but still meaningful dimensions for visualization and classification has grown rapidly over the past ...
  • Lunga, D; Ersoy, O (IEEE Xplore, 2013-06)
    Multidimensional embedding is a technique useful for characterizing spectral signature relations in hyperspectral images. However, such images consist of disjoint similar spectral classes that are spatially sensitive, thus ...
  • Zhang, Y; Yangz, HL; Lunga, D; Prasad, S; Crawford, M (2014-06)
    Manifold learning techniques have demonstrated various levels of success in their ability to represent spectral signature characteristics in hyperspectral imagery. Such images consists of spectral features with very subtle ...
  • Lunga, D; Ersoy, O (IEEE Xplore, 2012-07)
    In hyperspectral imagery, low-dimensional representations are sought in order to explain well the nonlinear characteristics that are hidden in high-dimensional spectral channels. While many algorithms have been proposed ...