Target Detection With Spatial-Spectral Adaptive Sample Generation and Deep Metric Learning for Hyperspectral Imagery

In hyperspectral target detection, the conventional metric learning-based algorithms provide unique advantages in detecting targets as they do not require specific assumptions and adapt to the condition of limited training samples. Nevertheless, they usually learn a linear transformation for metric space, which is unable to capture nonlinear mapping where the hyperspectral imageries possess, especially occurs in the spectra variability and nonlinear mixing problems. To alleviate this limitation, this study investigates a new spatial-spectral adaptive sample generation and deep metric learning-based method for hyperspectral target detection (denoted as DMLTD). The proposed DMLTD employs a spatial-spectral adaptive sample generation strategy and subpixel synthetic method for background sample generation and target sample augmentation, respectively. With sufficient samples, the proposed DMLTD trains a deep discriminative metric learning network to learn hierarchical nonlinear mappings, so that to address the spectra variability and nonlinear mixing problems, thus exploiting discriminative information between targets and backgrounds for detection. Experiments and analyses conducted on three real-world hyperspectral datasets indicate that our DMLTD yields competitive performance in hyperspectral image target detection.