3D-Hybrid Convolutional Autoencoder Model for Hyperspectral Satellite Data Compression

This work addresses the challenge of including the spatial dimension into the autoencoder models for lossy compression of different spatially independent and unknown hyperspectral datasets acquired by space-borne hyperspectral sensors. We propose two different 3D-Hybrid Convolutional Autoencoder models with increased compression rates compared to 1D methods that can compress and reconstruct hyperspectral data with arbitrary spectral dimensionality. The architecture of the first 3D-Hybrid model consists of the A1D-CAE in combination with the 2D-CAE. The second 3D-Hybrid model includes the adaptive 1D-CAE and a 3D-CAE. The evaluation of the reconstruction accuracy is measured by comparing the spectral angle and the peak signal-to-noise ratio between the original and the reconstructed data and structural similarity index measure. We show the high transferability and generalizability of our 3D-Hybrid models on different PRISMA datasets. The 3D-Hybrid model is compared with the SSCNet
2D
based on a 2D-CAE and a 3D-CAE model. The findings of this study contribute to understanding the strengths and limitations of machine learning-based compression methods for jointly compressing spectral and spatial information.