Classification of hyperspectral images by tensor modeling and additive morphological decomposition

PATTERN RECOGNITION

卷:46

Article

Velasco-Forero, Santiago¹  Angulo, Jesus^1,2 

[1] MINES Paristech, CMM, Ctr Morphol Math Math & Syst, Paris, France

[2] MINES Paristech, Ctr Math Morphol, Dept Math & Syst, Paris, France

关键词: Hyperspectral images;    Mathematical morphology;    Pixelwise classification;    Tensor modeling;   

DOI  :  10.1016/j.patcog.2012.08.011

来源: Elsevier

【 摘 要 】

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. (c) 2012 Elsevier Ltd. All rights reserved.

【 授权许可】

Free