Adaptive SimSiam Self-Supervised Networks with Hybrid PIO for Hyperspectral Image Classification

Hyperspectral imaging (HSI) provides rich spectral information across hundreds of bands, offering significant potential for fine-grained classification in remote sensing. However, the high dimensionality of HSI data and the scarcity of labeled samples remain persistent challenges. In this paper, we present ASSIAM, a unified framework that combines Hybrid Pigeon-Inspired Optimization (PIO) for spectral band selection with an Adaptive SimSiam self-supervised learning strategy. The Hybrid-PIO mechanism efficiently identifies informative bands by coupling global heuristic search with variance-preserving reduction, while the Adaptive SimSiam model - supported by transfer learning - learns robust spectral-spatial representations from unlabeled data. Extensive experiments on six benchmark HSI datasets, including Salinas, Pavia University, and Indian Pines, demonstrate that ASSIAM achieves consistent improvements over existing methods, with overall classification accuracies reaching up to 99.01%. These results highlight the benefits of integrating bio-inspired optimization with contrastive self-supervised learning to develop scalable and label-efficient solutions for hyperspectral image classification.