ProgramsView all programs
From the news
- Chancellor Amma Addresses the Parliament of World’s Religions
- Amrita Students Qualify for the European Mars Rover Challenge
Publication Type : Journal Article, Conference Paper
Publisher : Communications in Computer and Information Science
Source : Communications in Computer and Information Science, Springer Verlag, Volume 968, p.205-216 (2019)
ISBN : 9789811357572
Keywords : image analysis, Low Multilinear Rank Approximation, Multilinear singular value decompositions, Pixels, Reconstruction error, Reflectance spectrum, Reflection, Remote sensing, Remote sensing images, Singular value decomposition, Spectroscopy, Tensor decomposition, Tensors
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electrical and Electronics
Year : 2019
Abstract : 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. © 2019, Springer Nature Singapore Pte Ltd.
Cite this Research Publication : R. K. Renu, Sowmya, and Dr. Soman K. P., “Pre-processed hyperspectral image analysis using tensor decomposition techniques”, in Communications in Computer and Information Science, 2019, vol. 968, pp. 205-216.