Hyperspectral Target Detection: Learning Faithful Background Representations via Orthogonal Subspace-Guided Variational Autoencoder

Hyperspectral image (HSI) target detection plays a pivotal role in both military and civilian sectors. Nevertheless, this task is fraught with challenges because of the limited availability of target samples and the intricate nature of the background within real-world HSIs. In this study, we present an innovative background learning model based on the orthogonal subspace-guided variational autoencoder, tailored to discern the background distribution in hyperspectral imagery. Given the scarcity of target samples, our model is exclusively trained on background spectral samples, enabling precise modeling of the background distribution. The crux of our approach lies in detecting disparities between the reconstructed HSI and the original HSI, providing a mechanism for faithful target identification. To procure background samples, a coarse detection of the test HSI is first conducted. However, this process proves challenging, as obtaining pristine background pixels is a formidable task. To mitigate the influence of suspicious target samples on the background reconstruction, we employ orthogonal subspace loss on the reconstructed HSI. Extensive experiments conducted on four real-world HSIs substantiate that the proposed framework performs highly competitively and the results outperform other state-of-the-art HSI target detection methods. The source codes of this study are available at https://github.com/CX-He/OS-VAE.