Hyper Spectral Image compression using Higher Order Orthogonal Iteration Tucker decomposition

Hyper spectral images need more memory to store, process, and transmit due to the large amount of information they carry. One of the most effective way to compress hyper spectral images is to represent them as a three-dimensional tensor. Tensors are multi-dimensional Structures. Tensors have a wide...

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Vydáno v:Annual IEEE India Conference s. 1 - 7
Hlavní autoři: Sucharitha, B., Anitha Sheela, K.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 24.11.2022
Témata:
ALS
HOOI
HOSVD
Hyper spectral Image
Image coding
Memory management
PSNR
Simulation
SSIM
Tensor
Tensors
Termination of employment
Three-dimensional displays
Transforms
ISSN:2325-9418
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Popis
Shrnutí:Hyper spectral images need more memory to store, process, and transmit due to the large amount of information they carry. One of the most effective way to compress hyper spectral images is to represent them as a three-dimensional tensor. Tensors are multi-dimensional Structures. Tensors have a wide range of applications in numerical linear algebra, chemometrics, data mining, signal processing, statics, data mining, and machine learning. For dimensional reduction of tensors, many tensor decomposition methods have been developed, which we can adapt to Hyper spectral image compression. The proposed method uses Discrete Wavelet Transform and Higher-Order Orthogonal Iteration Tucker Decomposition method to compress the Hyper spectral Image. The simulation results were compared with 3 more tensor decomposition techniques. Four real hyper spectral images were used in the experimentation: Pavia University (610 X 340 X 103), Indian Pines adjusted (145 x 145 x 200), Salinas image (512 x 217 x 224), and Abu-beach (150 X 150 X 102). After processing, the perceptional quality of the Hyper spectral images is evaluated using PSNR and SSIM. When compared to the other three methods, the proposed method offers good PSNR and SSIM with High Compression.
ISSN:2325-9418
DOI:10.1109/INDICON56171.2022.10040093