Research on infrared hyperspectral band selection algorithm based on autoencoder

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.