PTGAN: A Proposal-Weighted Two-Stage GAN with Attention for Hyperspectral Target Detection
Haonan Qin, Weiying Xie, Yunsong Li, Kai Jiang, Jie Lei, Qian Du
Abstract
In this paper, a proposal-weighted two-stage generative adversarial network (GAN) with attention mechanism is proposed for hyperspectral target detection (HTD). PTGAN leverages GAN to estimate spectral background distribution and realize mapping from the latent space to the spectral space. Meanwhile, PTGAN conducts the reversed mapping through latent-spectral-latent and spectral-latent-spectral learning. On this basis, PTGAN implements accurate reconstruction of background spectrum via latent space. Therefore, targets of interest can be detected through larger pixel-level reconstruction error. In particular, the variance attention module is designed to make full use of global information among spectral bands to selectively emphasize channel-wise spectral features. Furthermore, a proposal-weighted strategy in a two-stage manner reduces the false alarm of detection by refining the previous detection proposal. Finally, exponential nonlinear fusion combines the discriminative feature from two stages to suppress the background. Extensive experiments on two real hyperspectral images (HSIs) verify the effectiveness of PTGAN.