Investigating the influence of hyperspectral data compression on spectral unmixing

Jannick Kuester; Johannes Anastasiadis; Wolfgang Middelmann; Michael Heizmann

doi:10.1117/12.2636129

26 October 2022 Investigating the influence of hyperspectral data compression on spectral unmixing

Jannick Kuester, Johannes Anastasiadis, Wolfgang Middelmann, Michael Heizmann

Proceedings Volume 12267, Image and Signal Processing for Remote Sensing XXVIII; 122670H (2022) https://doi.org/10.1117/12.2636129
Event: SPIE Remote Sensing, 2022, Berlin, Germany

Abstract

This work addresses the problem of hyperspectral data compression and the evaluation of the reconstruction quality for different compression rates. Data compression is intended to transmit the enormous amount of data created by hyperspectral sensors efficiently. The information loss due to the compression process is evaluated by the complex task of spectral unmixing. We propose an improved 1D-Convolutional Autoencoder architecture with different compression rates for lossy hyperspectral data compression. Furthermore, we evaluate the reconstruction by applying metrics such as SNR and SA and compare them to the spectral unmixing results.

Conference Presentation

Citation Download Citation

Jannick Kuester, Johannes Anastasiadis, Wolfgang Middelmann, and Michael Heizmann "Investigating the influence of hyperspectral data compression on spectral unmixing", Proc. SPIE 12267, Image and Signal Processing for Remote Sensing XXVIII, 122670H (26 October 2022); https://doi.org/10.1117/12.2636129

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