  The hyperspectral pixel unmixing aims to find the underlying materials
(endmembers) and their proportions (abundances) in pixels of a hyperspectral
image. This work extends the Latent Dirichlet Variational Autoencoder (LDVAE)
pixel unmixing scheme by taking into account local spatial context while
performing pixel unmixing. The proposed method uses an isotropic convolutional
neural network with spatial attention to encode pixels as a dirichlet
distribution over endmembers. We have evaluated our model on Samson, Hydice
Urban, Cuprite, and OnTech-HSI-Syn-21 datasets. Our model also leverages the
transfer learning paradigm for Cuprite Dataset, where we train the model on
synthetic data and evaluate it on the real-world data. The results suggest that
incorporating spatial context improves both endmember extraction and abundance
estimation.
