Adaptive Band Selection and Spectral–Spatial Collaborative Network for Fine-Grained Hyperspectral Identification of Archaeological Strata

Archaeological stratigraphy serves as the core foundation for establishing archaeological chronological sequences, clarifying ancient human activity patterns, and revealing the evolution of human–land relationships. Current field stratigraphic delineation mainly relies on manual visual observation and empirical judgement, which suffers from strong subjectivity and poor repeatability. It is difficult to accurately identify quasi-homogeneous archaeological strata with similar paedogenic backgrounds and soil characteristics, easily resulting in stratigraphic misclassification and boundary misjudgement. Hyperspectral imaging technology can effectively capture subtle spectral differences in soil physicochemical components and support the refined identification of homogeneous strata, whereas its practical application is restricted by high data dimensionality, severe band redundancy, and insufficient targeted extractions of effective features. Taking five typical archaeological deposits, including Warring States–Han, Song–Yuan, and Ming–Qing cultural layers, ash pits, and undisturbed subsoil, this study constructs a spectral–spatial collaborative deep learning model (ABS-Net) with an adaptive band selection module based on field-measured hyperspectral data of archaeological profiles. The proposed model realises the automatic mining of subtle spectral features and intelligent stratigraphic classification. Comparative experiments with multiple state-of-the-art hyperspectral classification models demonstrate that ABS-Net achieves competitive and stable classification accuracy. This study verifies the feasibility and application potential of combining hyperspectral remote sensing with deep learning for intelligent stratigraphic classification, providing a novel technical reference for intelligent stratigraphic interpretation in field archaeology.