Linear prediction band selection based on Schmidt orthogonalization for hyperspectral image

Huihui Ju; Qingbao Liu; Hui Gao; Yang Yang

doi:10.1117/12.2674290

28 April 2023 Linear prediction band selection based on Schmidt orthogonalization for hyperspectral image

Huihui Ju, Qingbao Liu, Hui Gao, Yang Yang

Proceedings Volume 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022); 126260U (2023) https://doi.org/10.1117/12.2674290
Event: International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 2022, Zhuhai, China

Abstract

Band selection is an important method for hyperspectral image(HSI) dimension reduction, which can greatly reduce the complexity of HSI analysis. Linear prediction algorithm is an unsupervised band selection algorithm with good classification effect. This paper uses Schmidt’s orthogonalization to improve it. Experiments prove that the optimized algorithm is consistent with linear prediction algorithm in band selection but much more efficient and the complexity of the optimized algorithm is in proportion to the number of selected bands.

Citation Download Citation

Huihui Ju, Qingbao Liu, Hui Gao, and Yang Yang "Linear prediction band selection based on Schmidt orthogonalization for hyperspectral image", Proc. SPIE 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 126260U (28 April 2023); https://doi.org/10.1117/12.2674290

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KEYWORDS

Matrices

Hyperspectral imaging

Reconstruction algorithms

Algorithms

Correlation coefficients

Error analysis

Mathematical optimization

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