Spectral unmixing with deep neural networks

Francis Doumet; Migel Tissera

doi:10.1117/12.2652623

14 June 2023 Spectral unmixing with deep neural networks

Francis Doumet, Migel Tissera

Proceedings Volume PC12519, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXIX ; PC1251905 (2023) https://doi.org/10.1117/12.2652623
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

The work presented introduces a deep-learning based technique to spectrally unmix data containing more than one endmember. It uses a novel loss function with a soft-attenuation mechanism leading the neural network to focus on visual features of the input spectra. A Deep Neural Network was developed to detect Ammonium Nitrate in Visible to Near Infrared (VNIR) and Short Wave Infrared (SWIR) co-aligned aerial hyperspectral imagery. We compare the target detection accuracy of our method, against a well-known classical method referred to as the Adaptive Cosine Estimation (ACE). We show that our DNN based method outperforms ACE by two-orders of magnitude.

Conference Presentation

Citation Download Citation

Francis Doumet and Migel Tissera "Spectral unmixing with deep neural networks", Proc. SPIE PC12519, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXIX , PC1251905 (14 June 2023); https://doi.org/10.1117/12.2652623

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