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.

通过利用异常对象与其局部背景的偏差特征，我们将异常检测任务重新定义为基于偏差关系的无向双层图，其中异常得分建模为给定背景模式和正常对象的条件概率，进而将学习目标表达为条件概率排序问题。我们在数据、架构和优化方面设计了一种实例化表达方法，并通过使用模拟的光谱和空间异常驱动实例化架构，通过直接优化条件概率排序来验证了该模型在包括高光谱、可见光、合成孔径雷达（SAR）、红外和低光等五种模态下的统一检测能力。