A High Precision Parallel Principal Skewness Analysis Algorithm and Its Application to Remote Sensing Images

WANG Dahu; LIU Chang; WANG Jian; YAO Kai; ZHANG Zhen

doi:10.11999/JEIT220960

Volume 45 Issue 10

Oct. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(10): 3492-3501

WANG Dahu, LIU Chang, WANG Jian, YAO Kai, ZHANG Zhen. A High Precision Parallel Principal Skewness Analysis Algorithm and Its Application to Remote Sensing Images[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3492-3501. doi: 10.11999/JEIT220960

Citation:

WANG Dahu, LIU Chang, WANG Jian, YAO Kai, ZHANG Zhen. A High Precision Parallel Principal Skewness Analysis Algorithm and Its Application to Remote Sensing Images[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3492-3501. doi: 10.11999/JEIT220960

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A High Precision Parallel Principal Skewness Analysis Algorithm and Its Application to Remote Sensing Images

doi: 10.11999/JEIT220960

WANG Dahu^{1, 2},
LIU Chang^{1, 2
,
,},
WANG Jian^{1, 2},
YAO Kai^{1, 2},
ZHANG Zhen³

1.
Institute of Space Information Innovation, Chinese Academy of Sciences, Beijing 100190, China
2.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100094, China
3.
Chinese People's Liberation Army Aviation Academy, Beijing 101123, China

Received Date: 2022-07-18
Accepted Date: 2022-12-20
Rev Recd Date: 2022-11-13

Available Online: 2022-12-23

Publish Date: 2023-10-31

Abstract

Abstract

Principal Skewness Analysis (PSA), as a third-order extension of Principal Component Analysis (PCA), is often used for blind image separation, SAR image denoising, and hyperspectral feature extraction. However, the existing PSA algorithm can only obtain approximate solutions, which will affect the accuracy of subsequent image processing. In view of this problem, a high-precision Parallel Principal Skewness Analysis (PPSA) algorithm based on the existing PSA algorithm is proposed. The PPSA algorithm considers fully the data structure, and selects the eigenvectors of all slices of the co-skewness tensor as the initial value of the iteration, which can accurately obtain the actual solution. Simulation experiments and actual remote sensing image experiments verify the effectiveness and superiority of the PSA algorithm.
- Image separation,
- Image denoising,
- Principal Skewness Analysis (PSA),
- High precision,
- Parallel,
- Feature extraction

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References(18)

References

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