Pre-processed Hyperspectral Image Analysis Using Tensor Decomposition Techniques

  • R. K. Renu
  • V. Sowmya
  • K. P. Soman
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 968)


Hyperspectral remote sensing image analysis has always been a challenging task and hence there are several techniques employed for exploring the images. Recent approaches include visualizing hyperspectral images as third order tensors and processing using various tensor decomposition methods. This paper focuses on behavioural analysis of hyperspectral images processed with various decompositions. The experiments includes processing raw hyperspectral image and pre-processed hyperspectral image with tensor decomposition methods such as, Multilinear Singular Value Decomposition and Low Multilinear Rank Approximation technique. The results are projected based on relative reconstruction error, classification and pixel reflectance spectrums. The analysis provides correlated experimental results, which emphasizes the need of pre-processing for hyperspectral images and the trend followed by the tensor decomposition methods.


Remote sensing image Tensor decomposition Multilinear Singular Value Decomposition Low Multilinear Rank Approximation Relative reconstruction error Pixel reflectance spectrums 


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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Center for Computational Engineering and Networking (CEN)Amrita School of Engineering, Amrita Vishwa VidyapeethamCoimbatoreIndia

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