学习用于遥感影像的跨模态异常检测器
Learning a Cross-modality Anomaly Detector for Remote Sensing Imagery
AI总结：
该研究将遥感异常检测的学习目标从变化背景分布转为一致偏差度量，提出像素级与特征级大间隔损失及异常模拟策略，实现五种遥感模态的零样本跨模态异常检测。
摘要：
遥感异常检测器能够发现偏离背景的目标，并将其作为地球监测的潜在目标。鉴于地球异常类型的多样性，设计一种具备跨模态检测能力的迁移模型，应当具有较高成本效益，并能灵活适应新的地球观测数据源和异常类型。然而，当前异常检测器旨在学习特定的背景分布，训练后的模型无法迁移到未见过的图像。受用于分数排序的偏差度量具有一致性且独立于图像分布这一事实启发，本研究利用了从变化的背景分布到一致偏差度量的学习目标转换。我们从理论上证明，带标签样本中的大间隔条件确保了所学偏差度量的迁移能力。为满足该条件，分别提出了用于像素级和特征级偏差排序的两种大间隔损失。由于真实异常难以获取，设计了异常模拟策略来计算模型损失。通过对偏差度量进行大间隔学习，训练后的模型以零样本方式在高光谱、可见光、合成孔径雷达（SAR）、红外和低光五种模态中实现了跨模态检测能力。
英文摘要：
Remote sensing anomaly detector can find the objects deviating from the background as potential targets for Earth monitoring. Given the diversity in earth anomaly types, designing a transferring model with cross-modality detection ability should be cost-effective and flexible to new earth observation sources and anomaly types. However, the current anomaly detectors aim to learn the certain background distribution, the trained model cannot be transferred to unseen images. Inspired by the fact that the deviation metric for score ranking is consistent and independent from the image distribution, this study exploits the learning target conversion from the varying background distribution to the consistent deviation metric. We theoretically prove that the large-margin condition in labeled samples ensures the transferring ability of learned deviation metric. To satisfy this condition, two large margin losses for pixel-level and feature-level deviation ranking are proposed respectively. Since the real anomalies are difficult to acquire, anomaly simulation strategies are designed to compute the model loss. With the large-margin learning for deviation metric, the trained model achieves cross-modality detection ability in five modalities including hyperspectral, visible light, synthetic aperture radar (SAR), infrared and low-light in zero-shot manner.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
