SCA-Net: A Self-Correcting Two-Layer Autoencoder for Hyper-spectral Unmixing

TL;DR

This paper introduces SCA-Net, a simple two-layer autoencoder that effectively unmixes hyperspectral data, outperforming complex neural models in accuracy, robustness, and requiring minimal prior assumptions.

Contribution

The paper presents the first demonstration that a minimal two-layer autoencoder can achieve state-of-the-art hyperspectral unmixing performance with self-correction capabilities.

Findings

SCA achieves error metrics orders of magnitude lower than previous methods.

SCA converges from random initialization to low-error solutions.

SCA outperforms existing methods on multiple benchmark datasets.

Abstract

Hyperspectral unmixing involves separating a pixel as a weighted combination of its constituent endmembers and corresponding fractional abundances, with the current state of the art results achieved by neural models on benchmark datasets. However, these networks are severely over-parameterized and consequently, the invariant endmember spectra extracted as decoder weights have a high variance over multiple runs. These approaches perform substantial post-processing while requiring an exact specification of the number of endmembers and specialized initialization of weights from other algorithms like VCA. We show for the first time that a two-layer autoencoder (SCA), with $2FK$ parameters ($F$ features, $K$ endmembers), achieves error metrics that are scales apart ($10^{-5})$ from previously reported values $(10^{-2})$. SCA converges to this low error solution starting from a random initialization of weights. We also show that SCA, based upon a bi-orthogonal representation, performs a self-correction when the number of endmembers are over-specified. Numerical experiments on Samson, Jasper, and Urban datasets demonstrate that SCA outperforms previously reported error metrics for all the cases while being robust to noise and outliers.