Küster, JannickJannickKüsterGroß, WolfgangWolfgangGroßMichel, AndreasAndreasMichelSchreiner, SimonSimonSchreinerMiddelmann, WolfgangWolfgangMiddelmannHeizmann, MichaelMichaelHeizmann2024-10-312024-10-312024https://publica.fraunhofer.de/handle/publica/47823210.1109/IGARSS53475.2024.10640551This 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.enconference paper