In this paper, we proposed to investigate unsupervised anomaly detection in Synthetic Aperture Radar (SAR) images. Our approach considers anomalies as abnormal patterns that deviate from their surroundings but without any prior knowledge of their characteristics. In the literature, most model-based algorithms face three main issues. First, the speckle noise corrupts the image and potentially leads to numerous false detections. Second, statistical approaches may exhibit deficiencies in modeling spatial correlation in SAR images. Finally, neural networks based on supervised learning approaches are not recommended due to the lack of annotated SAR data, notably for the class of abnormal patterns. Our proposed method aims to address these issues through a self-supervised algorithm. The speckle is first removed through the deep learning SAR2SAR algorithm. Then, an adversarial autoencoder is trained to reconstruct an anomaly-free SAR image. Finally, a change detection processing step is applied between the input and the output to detect anomalies. Experiments are performed to show the advantages of our method compared to the conventional Reed-Xiaoli algorithm, highlighting the importance of an efficient despeckling pre-processing step.
翻译:在本文中,我们建议调查合成孔径雷达(SAR)图像中未经监督的异常现象检测。 我们的方法认为异常现象是偏离其周围环境的异常模式,但却没有事先了解其特征。 在文献中,大多数基于模型的算法都面临三个主要问题。 首先,闪烁的噪音腐蚀了图像,并可能导致许多虚假检测。 其次,统计方法可能显示合成孔径雷达图像空间相关性模型存在缺陷。 最后,基于监督学习方法的神经网络由于缺少附加说明的合成孔径雷达数据,特别是异常模式类别的数据而没有被推荐。 我们建议的方法旨在通过一种自我监督的算法来解决这些问题。 光斑首先通过深入学习的SAR2SAR算法去掉。 然后, 对抗性自动编码器被训练来重建无异常的合成孔径雷达图像。 最后, 在输入和输出之间应用变化检测处理步骤来检测异常现象。 进行实验是为了显示我们的方法与常规 Reed- Xiaoli算法相比的优势, 突出了高效的预处理步骤的重要性。