  Hyperspectral image (HI) analysis approaches have recently become
increasingly complex and sophisticated. Recently, the combination of
spectral-spatial information and superpixel techniques have addressed some
hyperspectral data issues, such as the higher spatial variability of spectral
signatures and dimensionality of the data. However, most existing superpixel
approaches do not account for specific HI characteristics resulting from its
high spectral dimension. In this work, we propose a multiscale superpixel
method that is computationally efficient for processing hyperspectral data. The
Simple Linear Iterative Clustering (SLIC) oversegmentation algorithm, on which
the technique is based, has been extended hierarchically. Using a novel robust
homogeneity testing, the proposed hierarchical approach leads to superpixels of
variable sizes but with higher spectral homogeneity when compared to the
classical SLIC segmentation. For validation, the proposed homogeneity-based
hierarchical method was applied as a preprocessing step in the spectral
unmixing and classification tasks carried out using, respectively, the
Multiscale sparse Unmixing Algorithm (MUA) and the CNN-Enhanced Graph
Convolutional Network (CEGCN) methods. Simulation results with both synthetic
and real data show that the technique is competitive with state-of-the-art
solutions.
