  Hyperspectral anomaly detection (HAD) involves identifying the targets that
deviate spectrally from their surroundings, without prior knowledge. Recently,
deep learning based methods have become the mainstream HAD methods, due to
their powerful spatial-spectral feature extraction ability. However, the
current deep detection models are optimized to complete a proxy task (two-step
paradigm), such as background reconstruction or generation, rather than
achieving anomaly detection directly. This leads to suboptimal results and poor
transferability, which means that the deep model is trained and tested on the
same image. In this paper, an unsupervised transferred direct detection (TDD)
model is proposed, which is optimized directly for the anomaly detection task
(one-step paradigm) and has transferability. Specially, the TDD model is
optimized to identify the spectral deviation relationship according to the
anomaly definition. Compared to learning the specific background distribution
as most models do, the spectral deviation relationship is universal for
different images and guarantees the model transferability. To train the TDD
model in an unsupervised manner, an anomaly sample simulation strategy is
proposed to generate numerous pairs of anomaly samples. Furthermore, a global
self-attention module and a local self-attention module are designed to help
the model focus on the "spectrally deviating" relationship. The TDD model was
validated on four public HAD datasets. The results show that the proposed TDD
model can successfully overcome the limitation of traditional model training
and testing on a single image, and the model has a powerful detection ability
and excellent transferability.
