SpACNN-LDVAE: Spatial Attention Convolutional Latent Dirichlet
  Variational Autoencoder for Hyperspectral Pixel Unmixing
Soham Chitnis Kiran Mantripragada Faisal Z. Qureshi
The Hyperspectral Unxming problem is to find the pure spectral signal of the
underlying materials (endmembers) and their proportions (abundances). The
proposed method builds upon the recently proposed method, Latent Dirichlet
Variational Autoencoder (LDVAE). It assumes that abundances can be encoded as
Dirichlet Distributions while mixed pixels and endmembers are represented by
Multivariate Normal Distributions. However, LDVAE does not leverage spatial
information present in an HSI; we propose an Isotropic CNN encoder with spatial
attention to solve the hyperspectral unmixing problem. We 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 real-world data. We are
able to observe the improvement in the results for the endmember extraction and
abundance estimation by incorporating the spatial information. Code can be
found at https://github.com/faisalqureshi/cnn-ldvae