Convolutional Autoencoder Model for Hyperspectral Multi-Sensor Satellite Data Compression

This work addresses the challenge of transferability of autoencoder models for lossy compression of different spatially independent and unknown hyperspectral datasets acquired from different space sensor platforms. We propose an adaptive 1D convolutional autoencoder architecture that can compress and recover spectral signatures with different numbers of bands. We demonstrate the transferability of the 1D CAE to different sensors by applying different unknown hyperspectral datasets acquired by different sensor platforms. The evaluation of the reconstruction accuracy is measured by comparing the spectral angle and the signal-to-noise ratio between the original and the reconstructed data. We show the high transferability and generalizability of our A1D-CAE model for compression rates c R = 4 on different datasets from the satellite-based PRISMA, DESIS, EnMap and HYPSO-1 sensors. The results show that the proposed A1D-CAE architecture is capable of processing hyperspectral data from multiple sensor sources with different characteristics while achieving high reconstruction accuracy.