Graph Neural Network Based Interpretable Spectral Unmixing for Hyperspectral Unmixing Hyperspectral IIRS Data Onboard Chandrayaan-2 Mission

Although hyperspectral sensors are highly effective in mapping the minerals, the intimate nonlinear mixing and resolution tradeoff affect their effectiveness. In this regard, this study proposes a graph-based spectral unmixing strategy. The proposed approach leverages the advantages of both graph-based and deep learning based approaches. Additionally, the current study is a pioneer approach of using the graph-based approach for spectral unmixing. The spectral and spatial latent manifolds of the input patches are learned, and this information along with the endmember prior is used to formulate a graph-based representation. Further graph convolution approach is used to soft classify the spectra yielding fractional abundances. The results of the proposed approach on standard, synthetic and real-world data indicates that the proposed approach performs better than the state-of-the-art unmixing approaches. Moreover, the graph-based representations make the approach interpretable and facilitate the consideration of the spatial autocorrelation.