Earth Observation imagery can capture rare and unusual events, such as disasters and major landscape changes, whose visual appearance contrasts with the usual observations. Deep models trained on common remote sensing data will output drastically different features for these out-of-distribution samples, compared to those closer to their training dataset. Detecting them could therefore help anticipate changes in the observations, either geographical or environmental. In this work, we show that the reconstruction error of diffusion models can effectively serve as unsupervised out-of-distribution detectors for remote sensing images, using them as a plausibility score. Moreover, we introduce ODEED, a novel reconstruction-based scorer using the probability-flow ODE of diffusion models. We validate it experimentally on SpaceNet 8 with various scenarios, such as classical OOD detection with geographical shift and near-OOD setups: pre/post-flood and non-flooded/flooded image recognition. We show that our ODEED scorer significantly outperforms other diffusion-based and discriminative baselines on the more challenging near-OOD scenarios of flood image detection, where OOD images are close to the distribution tail. We aim to pave the way towards better use of generative models for anomaly detection in remote sensing.

翻译：地球观测图像能够捕捉到罕见且异常的事件，如灾害和重大景观变化，其视觉特征与常规观测截然不同。针对常见遥感数据训练的深度模型，对于这些分布外样本所输出的特征，将与训练数据集内样本的特征存在显著差异。因此，检测这些分布外样本有助于预测观测中的地理或环境变化。在本工作中，我们证明扩散模型的重建误差可作为无监督的分布外检测器，通过将其作为合理性评分来有效处理遥感图像。此外，我们提出了一种基于概率流ODE的新型重建评分器——ODEED。我们在SpaceNet 8数据集上进行了实验验证，涵盖了多种场景，包括具有地理偏移的经典OOD检测、近OOD设置（即灾前/灾后图像识别以及非淹没/淹没图像识别）。结果表明，在更具挑战性的近OOD场景（如洪水图像检测，其中OOD图像接近分布尾部）中，我们的ODEED评分器显著优于其他基于扩散模型和判别式模型的基线方法。我们旨在为生成模型在遥感异常检测中的更优应用铺平道路。