skip to main content
Idioma:

Spectral-Spatial Hyperspectral Image Segmentation Using Subspace Multinomial Logistic Regression and Markov Random Fields

Jun Li ; Bioucas-Dias, J. M. ; Plaza, A.

IEEE transactions on geoscience and remote sensing, 2012-03, Vol.50 (3), p.809-823 [Periódico revisado por pares]

New York, NY: IEEE

Texto completo disponível

Citações Citado por
  • Título:
    Spectral-Spatial Hyperspectral Image Segmentation Using Subspace Multinomial Logistic Regression and Markov Random Fields
  • Autor: Jun Li ; Bioucas-Dias, J. M. ; Plaza, A.
  • Assuntos: Applied geophysics ; Earth sciences ; Earth, ocean, space ; Exact sciences and technology ; Hyperspectral image segmentation ; Hyperspectral imaging ; Image segmentation ; Internal geophysics ; Labeling ; Logistics ; Markov random field (MRF) ; multinomial logistic regression (MLR) ; Optimization ; subspace projection method ; Training
  • É parte de: IEEE transactions on geoscience and remote sensing, 2012-03, Vol.50 (3), p.809-823
  • Descrição: This paper introduces a new supervised segmentation algorithm for remotely sensed hyperspectral image data which integrates the spectral and spatial information in a Bayesian framework. A multinomial logistic regression (MLR) algorithm is first used to learn the posterior probability distributions from the spectral information, using a subspace projection method to better characterize noise and highly mixed pixels. Then, contextual information is included using a multilevel logistic Markov-Gibbs Markov random field prior. Finally, a maximum a posteriori segmentation is efficiently computed by the min-cut-based integer optimization algorithm. The proposed segmentation approach is experimentally evaluated using both simulated and real hyperspectral data sets, exhibiting state-of-the-art performance when compared with recently introduced hyperspectral image classification methods. The integration of subspace projection methods with the MLR algorithm, combined with the use of spatial-contextual information, represents an innovative contribution in the literature. This approach is shown to provide accurate characterization of hyperspectral imagery in both the spectral and the spatial domain.
  • Editor: New York, NY: IEEE
  • Idioma: Inglês
Links
Voltar para lista de resultados
Ir para página anteriorAnterior Resultado  9 AvançarIr para próxima página

  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

Buscando em bases de dados remotas. Favor aguardar.