Hyperspectral Pixel Unmixing with Latent Dirichlet Variational Autoencoder

TL;DR

This paper introduces a variational autoencoder-based method for hyperspectral pixel unmixing that models abundances with Dirichlet distributions and endmembers with Normal distributions, enabling transfer learning from synthetic to real data.

Contribution

The paper presents a novel VAE framework with a Dirichlet bottleneck for unmixing, capable of transfer learning from synthetic to real hyperspectral data, achieving state-of-the-art results.

Findings

Achieves state-of-the-art results on multiple benchmarks.

Effectively performs transfer learning from synthetic to real data.

Introduces a new synthetic dataset for hyperspectral unmixing.

Abstract

We present a method for hyperspectral pixel {\it unmixing}. The proposed method assumes that (1) {\it abundances} can be encoded as Dirichlet distributions and (2) spectra of {\it endmembers} can be represented as multivariate Normal distributions. The method solves the problem of abundance estimation and endmember extraction within a variational autoencoder setting where a Dirichlet bottleneck layer models the abundances, and the decoder performs endmember extraction. The proposed method can also leverage transfer learning paradigm, where the model is only trained on synthetic data containing pixels that are linear combinations of one or more endmembers of interest. In this case, we retrieve endmembers (spectra) from the United States Geological Survey Spectral Library. The model thus trained can be subsequently used to perform pixel unmixing on "real data" that contains a subset of the endmembers used to generated the synthetic data. The model achieves state-of-the-art results on several benchmarks: Cuprite, Urban Hydice and Samson. We also present new synthetic dataset, OnTech-HSI-Syn-21, that can be used to study hyperspectral pixel unmixing methods. We showcase the transfer learning capabilities of the proposed model on Cuprite and OnTech-HSI-Syn-21 datasets. In summary, the proposed method can be applied for pixel unmixing a variety of domains, including agriculture, forestry, mineralogy, analysis of materials, healthcare, etc. Additionally, the proposed method eschews the need for labelled data for training by leveraging the transfer learning paradigm, where the model is trained on synthetic data generated using the endmembers present in the "real" data.