Unsupervised Bayesian Subpixel Mapping Autoencoder Network for Hyperspectral Images

Unsupervised subpixel mapping (SPM) of hyperspectral image (HSI) is a challenging task due to the difficulties to integrate different prior information and model constraints into a coherent framework. This paper presents a Bayesian neural network for unsupervised HSI SPM, which has the following characteristics. First, the deep image prior (DIP) achieved by a fully convolutional neural network (FCNN) is used to model the spatial correlation efficiently and adaptively in the subpixel label domain. Second, a discrete spectral mixture model (DSMM) is designed to leverage the forward model for enhanced SPM. Third, an auto-encoder architecture is designed to integrate the FCNN and the DSMM to allow efficient unsupervised representational learning using both data and knowledge. Fourth, an expectation-maximization approach is designed to solve the resulting maximum a posteriori problem, where a purified means approach extracts endmembers, and the gradient descent approach updates FCNN parameters for subpixel label estimation. Comparative experiments on both real and simulated HSIs demonstrate that the proposed method outperforms other state-of-the-art methods in terms of both numerical accuracies and visual subpixel mapping results.