Hyperspectral Target Detection Using Diffusion Model and Convolutional Gated Linear Unit

Deep learning can effectively extract latent information from data to enhance target-background separation in hyperspectral target detection (HTD). However, these models typically require extensive labeled samples, while available target spectra in hyperspectral images (HSIs) are scarce. In addition, existing deep models struggle with target detection in complex backgrounds due to subtle spectral differences. To address these issues, we propose a novel HTD method based on diffusion model and convolutional gated linear unit (HTD-DMCG). First, the diffusion model is integrated with MixUp for data augmentation to generate a diverse and sufficiently large sample set. Next, a transformer architecture using a convolutional gated linear unit (GLU) is designed to effectively capture global dependencies and local feature correlations, leading to more discriminative feature representations. In addition, a new target aggregation and background separation loss is introduced, which emphasizes target sample aggregation while increasing the distance between targets and background samples to enhance separability. The HTD-DMCG method is compared against classical and state-of-the-art HTD methods on four real HSI datasets. Extensive experiments show that it can effectively outperform existing methods in target detection performance. The code is available at https://github.com/Li-ZK/ HTD-DMCG