A Unified Remote Sensing Anomaly Detector Across Modalities and Scenes
  via Deviation Relationship Learning
Jingtao Li Xinyu Wang Hengwei Zhao Liangpei Zhang Yanfei Zhong
Remote sensing anomaly detector can find the objects deviating from the
background as potential targets. Given the diversity in earth anomaly types, a
unified anomaly detector across modalities and scenes should be cost-effective
and flexible to new earth observation sources and anomaly types. However, the
current anomaly detectors are limited to a single modality and single scene,
since they aim to learn the varying background distribution. Motivated by the
universal anomaly deviation pattern, in that anomalies exhibit deviations from
their local context, we exploit this characteristic to build a unified anomaly
detector. Firstly, we reformulate the anomaly detection task as an undirected
bilayer graph based on the deviation relationship, where the anomaly score is
modeled as the conditional probability, given the pattern of the background and
normal objects. The learning objective is then expressed as a conditional
probability ranking problem. Furthermore, we design an instantiation of the
reformulation in the data, architecture, and optimization aspects. Simulated
spectral and spatial anomalies drive the instantiated architecture. The model
is optimized directly for the conditional probability ranking. The proposed
model was validated in five modalities including the hyperspectral, visible
light, synthetic aperture radar (SAR), infrared and low light to show its
unified detection ability.