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
this https URL

本文分析了人类视觉感知下的高光谱影像特征，并首次将异常检测 (HAD) 的解决过程转移到了更稳健的特征空间。我们提出了一种小目标感知检测器 (STAD)，它引入了显著性图来捕捉更接近人类视觉感知的高光谱影像特征。此外，我们还提出了一种全连接网络到卷积网络的知识蒸馏策略，以适应 HAD 算法应用于边缘设备的可能性。我们在 HAD100 训练集上训练了网络，并在 HAD100 测试集上验证了所提出方法。经过充分的实验证明，我们的方法在真实高光谱影像上表现出卓越的性能和独特的潜力。