  Hyperspectral anomaly detection (HAD) aims to localize pixel points whose
spectral features differ from the background. HAD is essential in scenarios of
unknown or camouflaged target features, such as water quality monitoring, crop
growth monitoring and camouflaged target detection, where prior information of
targets is difficult to obtain. Existing HAD methods aim to objectively detect
and distinguish background and anomalous spectra, which can be achieved almost
effortlessly by human perception. However, the underlying processes of human
visual perception are thought to be quite complex. In this paper, we analyze
hyperspectral image (HSI) features under human visual perception, and transfer
the solution process of HAD to the more robust feature space for the first
time. Specifically, we propose a small target aware detector (STAD), which
introduces saliency maps to capture HSI features closer to human visual
perception. STAD not only extracts more anomalous representations, but also
reduces the impact of low-confidence regions through a proposed small target
filter (STF). Furthermore, considering the possibility of HAD algorithms being
applied to edge devices, we propose a full connected network to convolutional
network knowledge distillation strategy. It can learn the spectral and spatial
features of the HSI while lightening the network. We train the network on the
HAD100 training set and validate the proposed method on the HAD100 test set.
Our method provides a new solution space for HAD that is closer to human visual
perception with high confidence. Sufficient experiments on real HSI with
multiple method comparisons demonstrate the excellent performance and unique
potential of the proposed method. The code is available at
https://github.com/majitao-xd/STAD-HAD.
