Near orthogonal discrete quaternion Fourier transform components via an optimal frequency rescaling approach

Hu, Lingyue; Ling, Bingo Wing‐Kuen; Ho, Charlotte Yuk-Fan; Huang, Guoheng

doi:10.1049/iet-spr.2020.0199

Cited by 2 publications

(1 citation statement)

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“…On the other hand, a quaternion valued number is a number having three imaginary valued components and one real valued component [14][15][16][17]. By grouping the color planes of a hyperspectral image into the groups with each group having four color planes, forming the quaternion valued matrices, applying the quaternion valued singular spectrum analysis to the quaternion valued matrices, selecting the two dimensional quaternion valued singular spectrum analysis components and reconstructing the hyperspectral image, the correlation among both the pixels within each color plane and the pixels across different color planes are exploited [18].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Enhancement by Two Dimensional Quaternion Valued Singular Spectrum Analysis for Object Recognition

Lin

Ling

et al. 2021

Remote Sensing

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This paper proposes a two dimensional quaternion valued singular spectrum analysis based method for enhancing the hyperspectral image. Here, the enhancement is for performing the object recognition, but neither for improving the visual quality nor suppressing the artifacts. In particular, the two dimensional quaternion valued singular spectrum analysis components are selected in such a way that the ratio of the interclass separation to the intraclass separation of the pixel vectors is maximized. Next, the support vector machine is employed for performing the object recognition. Compared to the conventional two dimensional real valued singular spectrum analysis based method where only the pixels in a color plane is exploited, the two dimensional quaternion valued singular spectrum analysis based method fuses four color planes together for performing the enhancement. Hence, both the spatial information among the pixels in the same color plane and the spectral information among various color planes are exploited. The computer numerical simulation results show that the overall classification accuracy based on our proposed method is higher than the two dimensional real valued singular spectrum analysis based method, the three dimensional singular spectrum analysis based method, the multivariate two dimensional singular spectrum analysis based method, the median filtering based method, the principal component analysis based method, the Tucker decomposition based method and the hybrid spectral convolutional neural network (hybrid SN) based method.

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Section: Introductionmentioning

confidence: 99%