Research on infrared hyperspectral band selection algorithm based on autoencoder

Chang Liu; Guangping Wang

doi:10.1117/12.3007251

18 December 2023 Research on infrared hyperspectral band selection algorithm based on autoencoder

Chang Liu, Guangping Wang

Proceedings Volume 12960, AOPC 2023: Infrared Devices and Infrared Technology; and Terahertz Technology and Applications; 129600D (2023) https://doi.org/10.1117/12.3007251
Event: Applied Optics and Photonics China 2023 (AOPC2023), 2023, Beijing, China

Abstract

This paper proposed an infrared hyperspectral band selection algorithm based on autoencoder Combining neural network, deep learning and other methods, an infrared hyperspectral band selection algorithm based on autoencoder is proposed to reduce the dimension of infrared hyperspectral images without loss of information. Encode infrared hyperspectral data to obtain dimensionality reduced data, decode the dimensionality reduced data to obtain reconstructed hyperspectral data, and use a band selection evaluation method based on average reconstruction error to evaluate the effectiveness of this band selection method. Based on the measured infrared hyperspectral data, the performance of this algorithm is compared with that of the band selection algorithm based on spatial dimension inter class separability and spectral dimension inter class separability. Experimental results have shown that the algorithm proposed in this paper outperforms the other two algorithms and has low reconstruction error in band selection results.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chang Liu and Guangping Wang "Research on infrared hyperspectral band selection algorithm based on autoencoder", Proc. SPIE 12960, AOPC 2023: Infrared Devices and Infrared Technology; and Terahertz Technology and Applications, 129600D (18 December 2023); https://doi.org/10.1117/12.3007251

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