Department of Mathematics
 Search | Help | Login | pdf version | printable version

Math @ Duke



Publications [#265126] of Guillermo Sapiro

Papers Published

  1. Castrodad, A; Xing, Z; Greer, J; Bosch, E; Carin, L; Sapiro, G, Discriminative sparse representations in hyperspectral imagery, Proceedings International Conference on Image Processing, Icip (December, 2010), pp. 1313-1316, IEEE, ISSN 1522-4880 [doi]
    (last updated on 2019/06/27)

    Recent advances in sparse modeling and dictionary learning for discriminative applications show high potential for numerous classification tasks. In this paper, we show that highly accurate material classification from hyperspectral imagery (HSI) can be obtained with these models, even when the data is reconstructed from a very small percentage of the original image samples. The proposed supervised HSI classification is performed using a measure that accounts for both reconstruction errors and sparsity levels for sparse representations based on class-dependent learned dictionaries. Combining the dictionaries learned for the different materials, a linear mixing model is derived for sub-pixel classification. Results with real hyperspectral data cubes are shown both for urban and non-urban terrain. © 2010 IEEE.
ph: 919.660.2800
fax: 919.660.2821

Mathematics Department
Duke University, Box 90320
Durham, NC 27708-0320