  Hyperspectral anomaly detection (HAD) is widely used in Earth observation and
deep space exploration. A major challenge for HAD is the complex background of
the input hyperspectral images (HSIs), resulting in anomalies confused in the
background. On the other hand, the lack of labeled samples for HSIs leads to
poor generalization of existing HAD methods. This paper starts the first
attempt to study a new and generalizable background learning problem without
labeled samples. We present a novel solution BSDM (background suppression
diffusion model) for HAD, which can simultaneously learn latent background
distributions and generalize to different datasets for suppressing complex
background. It is featured in three aspects: (1) For the complex background of
HSIs, we design pseudo background noise and learn the potential background
distribution in it with a diffusion model (DM). (2) For the generalizability
problem, we apply a statistical offset module so that the BSDM adapts to
datasets of different domains without labeling samples. (3) For achieving
background suppression, we innovatively improve the inference process of DM by
feeding the original HSIs into the denoising network, which removes the
background as noise. Our work paves a new background suppression way for HAD
that can improve HAD performance without the prerequisite of manually labeled
data. Assessments and generalization experiments of four HAD methods on several
real HSI datasets demonstrate the above three unique properties of the proposed
method. The code is available at https://github.com/majitao-xd/BSDM-HAD.
