Classification of hyperspectral images by tensor modeling and additive morphological decomposition

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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.

论文关键词:Hyperspectral images,Mathematical morphology,Pixelwise classification,Tensor modeling

论文评审过程:Received 1 February 2012, Revised 24 July 2012, Accepted 12 August 2012, Available online 31 August 2012.

论文官网地址:https://doi.org/10.1016/j.patcog.2012.08.011