Model Inspired Autoencoder for Unsupervised Hyperspectral Image Super-Resolution

TL;DR

This paper introduces an unsupervised, model-inspired autoencoder network for hyperspectral image super-resolution that integrates nonnegative matrix factorization, enabling effective fusion of low-resolution hyperspectral and high-resolution multispectral images.

Contribution

First to design a model-inspired deep autoencoder for unsupervised hyperspectral image super-resolution using NMF integration and pixel-wise fusion.

Findings

Effective on synthetic and real datasets

Outperforms existing unsupervised methods

Patch-based training reduces computational load

Abstract

This paper focuses on hyperspectral image (HSI) super-resolution that aims to fuse a low-spatial-resolution HSI and a high-spatial-resolution multispectral image to form a high-spatial-resolution HSI (HR-HSI). Existing deep learning-based approaches are mostly supervised that rely on a large number of labeled training samples, which is unrealistic. The commonly used model-based approaches are unsupervised and flexible but rely on hand-craft priors. Inspired by the specific properties of model, we make the first attempt to design a model inspired deep network for HSI super-resolution in an unsupervised manner. This approach consists of an implicit autoencoder network built on the target HR-HSI that treats each pixel as an individual sample. The nonnegative matrix factorization (NMF) of the target HR-HSI is integrated into the autoencoder network, where the two NMF parts, spectral and spatial matrices, are treated as decoder parameters and hidden outputs respectively. In the encoding stage, we present a pixel-wise fusion model to estimate hidden outputs directly, and then reformulate and unfold the model's algorithm to form the encoder network. With the specific architecture, the proposed network is similar to a manifold prior-based model, and can be trained patch by patch rather than the entire image. Moreover, we propose an additional unsupervised network to estimate the point spread function and spectral response function. Experimental results conducted on both synthetic and real datasets demonstrate the effectiveness of the proposed approach.