A Disentangled Variational Autoencoder for Prediction of Above Ground Biomass from Hyperspectral Data

The prediction of forest biophysical parameters is an important task in remote sensing for understanding global carbon cycle. Spectral remote sensing data are available globally at a relatively economical cost making them a viable resource for forest remote sensing. However, the main drawbacks associated with such data is the uncertainty of predictions and cluttered process of selecting band combinations from hyperspectral/multispectral data to produce spectral features for modelling. In this paper, we present an approach that exploits the latest developments in generative variational autoencoders (VAE) that produce disentangled representation from input data to assess the capability of hyperspectral data to model forest aboveground biomass (AGB). The proposed VAE generates a special kind of deep spectral features that are proportional to AGB. A modelling accuracy of R2 = 0.57 (cross-validated) was obtained by the proposed approach, thus pointing out the potential of hyperspectral data to model AGB using disentangled deep spectral features. The proposed approach also enables in bypassing the unreliable process of selecting band combinations to produce spectral features and shows good prospects for mapping global level biomass.