id: 06105618
dt: j
an: 06105618
au: Velasco-Forero, Santiago; Angulo, Jesus
ti: Classification of hyperspectral images by tensor modeling and additive
    morphological decomposition.
so: Pattern Recognition 46, No. 2, 566-577 (2013).
py: 2013
pu: Elsevier Science Ltd. (Pergamon), Oxford
la: EN
cc: 
ut: hyperspectral images; mathematical morphology; pixelwise classification;
    tensor modeling
ci: 
li: doi:10.1016/j.patcog.2012.08.011
ab: Summary: Pixel-wise classification in high-dimensional multivariate images
    is investigated. The proposed method deals with the joint use of
    spectral and spatial information provided in hyperspectral images.
    Additive morphological decomposition (AMD) based on morphological
    operators is proposed. AMD defines a scale-space decomposition for
    multivariate images without any loss of information. AMD is modeled as
    a tensor structure and tensor principal components analysis is compared
    as dimensional reduction algorithm versus classic approach.
    Experimental comparison shows that the proposed algorithm can provide
    better performance for the pixel classification of hyperspectral image
    than many other well-known techniques.
rv: