We propose a methodological framework to extract spatial features in hyperspectral imaging data and establish a link between these features and the spectral regions, capturing the observed structural patterns. The proposed approach consists of five main steps: (i) two-dimensional stationary wavelet transform (2D-SWT) is applied to a hyperspectral data cube, decomposing each single-channel image with a selected wavelet filter up to the maximum decomposition level; (ii) a gray-level co-occurrence matrix is calculated for every 2D-SWT image resulting from stage (i); (iii) distinctive spatial features are determined by computing morphological descriptors from each gray-level co-occurrence matrix; (iv) the morphological descriptors are rearranged in a two-dimensional data array; and (v) this data matrix is subjected to principal component analysis (PCA) for exploring the variability of the aforementioned descriptors across spectral channels. As a result, groups of spectral wavelengths associated to specific spatial features can be pointed out yielding a better understanding and interpretation of the data. In principle, this information can also be further exploited, for example, to improve the separation of pure spectral profiles in a multivariate curve resolution context.