ABSTRACT Hyperspectral imagery (HSI) creates a lot of applications in target or anomaly detection due to their rich spectral content. Generally, one scene of an HSI contains more than one class. Therefore, the Gaussian distribution assumption of the background fails. Furthermore, the high dimensionality of data makes background modelling more difficult by increasing redundancy and disturbances. In this paper, a segmented-distance based anomaly detection method is proposed for HSI. The proposed method is based on segmentation and takes advantage of the statistical properties of the segmented areas to suppress the false-alarms. In addition to that, nonlinear feature extraction based on convolutional stacked auto-encoder (SAE) neural networks are implemented to extract deep and nonlinear relations from the input data. Both 1-D and 2-D convolutional layers are investigated. The proposed method is tested on the three different datasets. The experimental results show that the integration of segmentation and deep feature extraction generally performs better than other state-of-the-art methods.

中文翻译：

---

一种基于分割和卷积神经网络算法的高光谱异常检测框架

摘要 高光谱图像 (HSI) 由于其丰富的光谱内容而在目标或异常检测中创造了许多应用。通常，一个 HSI 的一个场景包含多个类。因此，背景的高斯分布假设不成立。此外，数据的高维性通过增加冗余和干扰使背景建模更加困难。在本文中，提出了一种基于分段距离的 HSI 异常检测方法。该方法基于分割并利用分割区域的统计特性来抑制误报。除此之外，还实现了基于卷积堆叠自动编码器 (SAE) 神经网络的非线性特征提取，以从输入数据中提取深层非线性关系。研究了 1-D 和 2-D 卷积层。所提出的方法在三个不同的数据集上进行了测试。实验结果表明，分割和深度特征提取的集成通常比其他最先进的方法表现更好。