Low-rank tensor recovery (LRTR)-based feature extraction from authentic hyperspectral images (HSIs) has become widely employed to improve classification performance by removing sparse errors while preserving multidimensional structures. However, these techniques operate on either individual planar slices or unfolded matrix of each dimension, thereby overlooking spectral cross-channel correlation. Although quaternion-based low-rank recovery takes advantage of spectral cross-channel correlations, it often depends on spectral compression of global spectral bands, overlooking most spectral information, including the heterogeneity of various spectral regions and the homogeneity of regional spectrums. To address the challenges, this article proposes a super-spectrum parallel low-rank quaternion recovery (SS-PLRQR) model composed of a hybrid super-spectrum grouping strategy (HSSGS) and a parallel low-rank quaternion recovery (PLRQR) algorithm. HSSGS streamlines the algorithmic flow and provides necessary conditions for subsequent adaptive analysis by visually partitioning global bands into heterogeneous subregions. Following this, PLRQR effectively and independently eliminates noisy errors from each super-spectrum, utilizing fit parameter values tailored to the heterogeneity of different super-spectrums. It maintains the complete planar spatial structure and cross-channel correlation of homogeneous spectrums within the same quaternionic super-spectrum. Comparative experiments on three real-world HSIs demonstrate the proposed model’s remarkable applicability, effectiveness, and robustness to various noises in classification.