Unsupervised Progressive Domain Adaptation for Underwater Hyperspectral Target Detection Across Increasing Turbidity Levels

Detecting submerged objects becomes difficult when suspended particles alter spectral signatures and reduce target–background contrast. A detector trained in clear water can consequently lose discrimination and produce extensive background responses as turbidity increases. Here, we present a target-specific framework that couples progressive domain adaptation with object-level response calibration. A nonlinear unmixing autoencoder first learns the target representation from labeled clear-water observations. Cross-turbidity progressive domain adaptation (CTP-DA) then transfers model states, target prototypes, and prediction memory through ordered unlabeled domains. Risk-gated object-level background suppression (RG-OBS) subsequently limits background activation while protecting supported target regions. Across the L1–L5 target domains of the controlled brick-and-stone benchmark, the framework achieved an average precision of 0.9282. This exceeded the strongest external comparator evaluated here by 7.10 percentage points. Ablation experiments linked the ranking improvement to progressive adaptation and the reduction in background response to subsequent spatial calibration. Together, these findings support combining intermediate unlabeled domains with spatial target evidence to sustain underwater detection across controlled turbidity changes.