Spectral unmixing is a technique for remotely sensed image interpretation that expresses each (possibly mixed) pixel as a combination of pure spectral signatures (endmembers) and their fractional abundances. In this paper, we develop a new technique for unsupervised unmixing which is based on a deep autoencoder network (DAEN). Our newly developed DAEN consists of two parts. The first part of the network adopts stacked autoencoders (SAEs) to learn spectral signatures, so as to generate a good initialization for the unmixing process. In the second part of the network, a variational autoencoder (VAE) is employed to perform blind source separation, aimed at obtaining the endmember signatures and abundance fractions simultaneously. By taking advantage from the SAEs, the robustness of the proposed approach is remarkable as it can unmix data sets with outliers and low signal-to-noise ratio. Moreover, the multihidden layers of the VAE ensure the required constraints (nonnegativity and sum-to-one) when estimating the abundances. The effectiveness of the proposed method is evaluated using both synthetic and real hyperspectral data. When compared with other unmixing methods, the proposed approach demonstrates very competitive performance.

With the rising development of imaging spectroscopy, there is an increase in demand for images with high spectral, spatial, and temporal resolution. This study proposes a reconstruction from low spectral resolution multispectral imagery to higher spectral resolution hyperspectral imagery to overcome the problem of low spatial resolution and less availability of multi-temporal hyperspectral images. This study proposes a novel approach to dictionary learning based on nonlinear unmixing for dense mixtures and linear unmixing for sparse mixtures. Additionally, least square-based sparse coding has been applied in this framework to reconstruct high-resolution spectra from low-resolution spectra. The proposed method has been implemented on standard as well as real datasets and the performance of reconstruction has been validated through parametric techniques. The proposed method has been compared with states of the art methods like Joint Sparse & Low-Ranking Method and Derivative based Learning Method and it is observed that the proposed method outperforms the other methods. Nonlinear Spectral Unmixing has been performed and classification accuracy has been assessed on regenerated image data and observed to produce satisfactory output. In this study, the regenerated high spectral resolution image has been used for species-level classification and mapping of pure and mixed patches of mangrove species and the output compared with the Hyperion hyperspectral dataset of Henry Island, Sunderbans, India. Similarly, the proposed algorithm has also been applied on the Indian Pines, Salinas, Cuprite and Jasper Ridge datasets and the regenerated high spectral resolution images have been used for subpixel level target detection and classification.