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.

翻译：遥感异常检测器能够发现与背景存在偏差的物体作为潜在目标。鉴于地球异常类型的多样性，一种跨模态与场景的统一异常检测器应具备成本效益，并能灵活适应新型对地观测源及异常类型。然而，当前异常检测器受限于单一模态与单一场景，因其旨在学习变化的背景分布。受异常普遍存在的偏离模式启发——即异常与其局部上下文存在偏差——我们利用这一特性构建统一异常检测器。首先，我们将异常检测任务重新表述为基于偏差关系的无向双层图，其中异常得分被建模为给定背景与正常物体模式下的条件概率。学习目标随后被表达为条件概率排序问题。进一步地，我们从数据、架构与优化三方面设计了该重新表述的具体实现。模拟光谱与空间异常驱动了实例化架构。模型直接针对条件概率排序进行优化。所提模型在五类模态（包括高光谱、可见光、合成孔径雷达、红外与低照度）上得到验证，展示了其统一检测能力。