A Dual-Network Framework With Adversarial GMM Augmentation and Frequency-Mamba Fusion for Hyperspectral Target Detection

Hyperspectral target detection (HTD) involves identifying target pixels from complex backgrounds using known or inferred spectral signatures. With advances in hyperspectral imaging technology, HTD has found widespread applications in both military and civilian domains. However, it still faces challenges such as sample imbalance and spectral variability. To address these challenges, we propose a coherent pipeline that couples data, representation, and modeling. First, we develop AdvGMM, which fits a Gaussian mixture model (GMM) to high-confidence target spectra and applies adversarial reweighting against hard backgrounds to synthesize diverse, structurally constrained pseudotargets, thereby alleviating sample scarcity. Building on this, a frequency-domain adaptive fusion and Mamba-based enhanced encoder network (FAME-Net) is proposed to address the spectral variation and improve the discriminability of targets and backgrounds. FAME-Net comprises two key modules: a frequency-domain feature adaptive fusion (FDFAF) module that adaptively amplifies information-rich bands and integrates complementary frequency components while preserving the overall reflectance trend; and an efficient Mamba block that captures long-range spectral dependencies, avoids class confusion caused by similar local features, and converts the frequency-enhanced spectra into scalable, robust features. Extensive experiments on six benchmark datasets demonstrate that the proposed method outperforms state-of-the-art approaches under limited supervision, achieving superior detection robustness.