Hyperspectral Unmixing Using Deep Convolutional Autoencoders in a Supervised Scenario

Abstract

Hyperspectral unmixing (HSU) is an essential technique that aims to address the mixed pixels problem in hyperspectral imagery via estimating the abundance of each endmember at every pixel given the endmembers. This article introduces two approaches intending to solve the challenge of the mixed pixels using deep convolutional autoencoders (DCAEs), namely pixel-based DCAE, and cube-based DCAE. The former estimates abundances with the help of only spectral information, while the latter utilizes both spectral and spatial information which results in better unmixing performance. In the proposed frameworks, the weights of the decoder are set equal to the endmembers in order to address the issue in a supervised scenario. The proposed frameworks are also adapted to the VGG-Net that proved increasing depth with small convolution filters ( $\text{3}\times \text{3}$ ) leads to a considerable improvement. In other words, inspired by this idea, we utilize small and fixed kernels of size 3 in all layers of both proposed frameworks. The network is trained via the spectral information divergence objective function, and the dropout and regularization techniques are utilized to prevent overfitting. The superiority of the proposed frameworks is proven via conducting some experiments on both synthetic and real hyperspectral datasets and drawing a comparison with state-of-the-art methods. Moreover, the quantitative and visual evaluation of the proposed frameworks indicate the necessity of integrating spatial information into the HSU.