Publication Type : Conference Paper
Publisher : 2018 24th National Conference on Communications, NCC 2018
Source : 2018 24th National Conference on Communications, NCC 2018, Institute of Electrical and Electronics Engineers Inc., Hyderabad; India (2019)
Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061654567&doi=10.1109%2fNCC.2018.8600185&partnerID=40&md5=e7bfdfe475b69a39ca464cebee071552
ISBN : 9781538612248
Keywords : Computerized tomography, Efficiency, Image compression, Image reconstruction, Multilinear singular value decompositions, Pixels, Reconstructed image, Reflectance spectrum, Signal to noise ratio, Singular value decomposition, Spectral compression, Spectral efficiencies, Spectroscopy, Tensor decomposition, Tensors, Third-order tensors, Two dimensional images
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Verified : No
Year : 2019
Abstract : Hyperspectral images are large cubes of data which are commonly processed band-wise as two-dimensional image patches. This 2D processing might lead to loose the spectral efficiency contained in the image. Introducing Hyperspectral image as third-order tensors helps to preserve the spectral and spatial efficiency of the image. Multilinear Singular Value Decomposition (MLSVD) is an extension of Singular Value Decomposition (SVD) to 3D which can be used for compressing the image spatially and spectrally. The efficiency of compression is verified by reconstructing the image using Low Multilinear Rank Approximation (LMLRA). The proposed method has been validated with Signal to Noise Ratio (SNR), pixel reflectance spectrum and pixel-wise classification of the reconstructed image. © 2018 IEEE.
Cite this Research Publication : R. K. Renu, Sowmya, and Dr. Soman K. P., “Spatio-Spectral Compression and Analysis of Hyperspectral Images using Tensor Decomposition”, in 2018 24th National Conference on Communications, NCC 2018, Hyderabad; India, 2019.