Application of Hyperspectral Image Unmixing for Internet of Things

Abstract

Hyperspectral Unmixing (HU), also known as spectral mixture analysis, is a challenging problem that decomposes a mixed spectrum into a collection of endmembers and their abundance fractions. In this paper, we extended the autoencoder network (Palsson et al. in IEEE Access 6:25646–25656, [1]) for blind hyperspectral nonlinear unmixing. The proposed autoencoder architecture consists of two networks encoder and decoder. The encoder network has the same as the original architecture. The architecture of the decoder network was altered to handle nonlinear unmixing. Our proposed encoder network has four fully connected layers, each with the number of neurons equal to the dimension of the end members. Experiments were conducted using nonlinear synthetic dataset sampled from the USGS library, and the performance was evaluated using both SAD and SID metrics. Four categories of experiments were carried out; accuracy assessment, weight initialization techniques, learning rate, and robustness to the noise. Experimental results show that the proposed autoencoder outperforms traditional endmember extraction algorithms in nonlinear cases. Finally, we introduced the application of hyperspectral image unmixing algorithm in the Internet of things (IoT) environment.