论文标题
基于GAN的高光谱异常检测
GAN-based Hyperspectral Anomaly Detection
论文作者
论文摘要
在本文中，我们提出了一种基于基于基于的高光谱异常检测算法的生成对抗网络（GAN）。在提出的算法中，我们训练一个GAN模型，以生成尽可能接近原始背景图像的合成背景图像。通过从原始图像中减去合成图像，我们可以从高光谱图像中删除背景。通过在光谱差异图像上应用Reed-Xiaoli（RX）异常检测器（AD）来进行异常检测。在实验部分中，我们将我们提出的方法与基于合成和真实高光谱图像的经典RX，加权RX（WRX）和支持矢量数据描述（SVDD）的载体数据描述（SVDD）的异常检测器（DAEAD）方法（DAEAD）方法进行了比较。检测结果表明，我们提出的算法在基准中优于其他方法。
In this paper, we propose a generative adversarial network (GAN)-based hyperspectral anomaly detection algorithm. In the proposed algorithm, we train a GAN model to generate a synthetic background image which is close to the original background image as much as possible. By subtracting the synthetic image from the original one, we are able to remove the background from the hyperspectral image. Anomaly detection is performed by applying Reed-Xiaoli (RX) anomaly detector (AD) on the spectral difference image. In the experimental part, we compare our proposed method with the classical RX, Weighted-RX (WRX) and support vector data description (SVDD)-based anomaly detectors and deep autoencoder anomaly detection (DAEAD) method on synthetic and real hyperspectral images. The detection results show that our proposed algorithm outperforms the other methods in the benchmark.
