Adaptive Coarse-to-Fine Parameter Optimization for Incremental Hyperspectral Target Detection

Hyperspectral target detection effectively identifies fixed targets using specific spectral signatures but suffers from catastrophic forgetting when detecting multiple targets of interest within the same scene. Traditional data replay strategies may further exacerbate training instability due to mislabeled samples. To address these limitations, we propose an Adaptive Coarse-to-Fine Parameter Optimization framework (ACFPO) for incremental hyperspectral target detection, which enables stable continual learning via structural adaptation and parameter sensitivity–aware refinement. ACFPO formulates the task as a dual-stage process: coarse-grained matching and fine-grained detection. Specifically, an Adaptive Spectral Prior-Guided Coarse Matching (AS-PCM) module is designed to hierarchically organize detection tasks into semantic domains and construct intra- and inter-class spectral pairs for coarse-level alignment to adaptively select optimal submodels. Subsequently, a Distance-Aware Localized Fine-Grained Parameter Optimization (DA-LFPO) module is proposed to identify layer-wise sensitive parameters of the selected submodels by measuring spectral–spatial discrepancy, enabling selective retraining to preserve model stability on previously learned classes. By dynamically freezing non-sensitive parameters and optimizing critical modules, our approach mitigates inherent model drift and gradient conflicts in replay-based methods. Extensive experiments on three benchmark datasets demonstrate the superior performance of ACFPO, achieving a balanced trade-off between stability of the existing target and the adaptability of incremental targets. The code is available at https://github.com/Jiahuiqu/ACFPO.