目的 高光谱遥感中，通常利用像素的光谱特征来区分背景地物和异常目标，即通过二者之间的光谱差异来寻找图像中的异常像元。但传统的异常检测算法并未有效挖掘光谱的深层特征，高光谱图像中丰富的光谱信息没有被充分利用。针对这一问题，本文提出结合孪生神经网络和像素配对策略的高光谱图像异常检测方法，利用深度学习技术提取高光谱图像的深层非线性特征，提高异常检测精度。方法 采用像素配对的思想构建训练样本，与原始数据集相比，配对得到的新数据集数量呈指数增长，从而满足深度网络对数据集数量的需求。搭建含有特征提取模块和特征处理模块的孪生网络模型，其中，特征处理模块中的卷积层可以专注于提取像素对之间的差异特征，随后利用新的训练像素对数据集进行训练，并将训练好的分类模型固定参数，迁移至检测过程。用滑动双窗口策略对测试集进行配对处理，将测试像素对数据集送入网络模型，得到每个像素相较于周围背景像素的差异性分数，从而识别测试场景中的异常地物。结果 在异常检测的实验结果中，本文提出的孪生网络模型在San Diego数据集的两幅场景和ABU-Airport数据集的一幅场景上，得到的AUC （area under the curve）值分别为0.993 51、0.981 21和0.984 38，在3个测试集上的表现较传统方法和基于卷积神经网络的异常检测算法具有明显优势。结论 本文方法可以提取输入像素对的深层光谱特征，并根据其特征的差异性，让网络学习到二者的区分度，从而更好地赋予待测像素相对于周围背景的异常分数。本文方法相对于卷积神经网络的异常检测方法可以有效地降低虚警，与传统方法相比能够更加明显地突出异常目标，提高了检测率，同时也具有较强的鲁棒性。

Objective The continuous improvement in spectral resolution promotes the development and progress of hyperspectral remote sensing technology. Hyperspectral remote sensing technology has broad application scenarios and great application value and is a major research topic in the field of remote sensing. Anomaly detection of the hyperspectral image is an important branch in the field of hyperspectral remote sensing, and it is widely used in the industry, geological exploration, and other fields. The number of anomalies in the scene is usually small, and the spatial and spectral characteristics are different from the surrounding background. In the hyperspectral field, the spectral characteristics of pixels are usually used to distinguish the background and anomalous targets, that is, the anomalous pixels in the image are searched through the spectral differences. The detection methods based on a statistical model and traditional machine learning will have difficulty building the background model because of the complexity of background pixels. They will also have difficulty selecting the form of kernel function and building the background dictionary due to the lack of prior knowledge. Moreover, traditional anomaly detection algorithms do not effectively mine the deep features of the spectrum, and the rich spectral information in hyperspectral images is not fully utilized. Deep learning has great advantages in processing complex hyperspectral images, and anomaly detection method using deep learning is still a frontier area worthy of exploration and has gradually become the focus of research. Therefore, this study proposes a hyperspectral image anomaly detection method based on siamese neural network with pixel-pair strategy. It uses deep learning technology to extract the deep nonlinear features of hyperspectral images. This method aims to improve the accuracy of anomaly detection and promote the development of hyperspectral image processing and application technology. Method The method of anomaly detection in the hyperspectral image based on siamese neural network with pixel-pair feature (PPF-SNN) is divided into three steps. First, the idea of pixel-pair is adopted to amplify training samples because of the scarcity of hyperspectral data samples with real labels and the need for a large number of training data of the deep network model. Specifically, two pixels are randomly selected from the reference data containing multiple types of ground materials for matching. If they come from the same labeled class, then the pair is labeled as 0; if they come from different labeled classes, then the label is 1. Compared with the original datasets, the number of new datasets obtained by pairing increases exponentially to meet the demand of a deep network for the number of datasets. Second, we build a siamese network model with a feature extraction module and feature processing module. The branch network of the feature extraction module adopts the convolutional neural network (CNN) structure with weight sharing, which contains 10 convolutional layers. The feature processing module concatenates the input feature pairs and then extracts the difference features of pixel pairs through a convolutional layer, while the pixel pairs are classified through the fully connected layers. The module uses the extracted pixel pair features to measure the similarity of the input pairs. Then, the new training dataset is used to train the model, and the trained classification model is transferred to the detection process with fixed parameters. Third, the sliding dual-window strategy is used to pair the test set, and the test pixel pair dataset is sent to the network model. Next, the difference score of each pixel compared with the surrounding background pixels is obtained. If the score is close to 1, then the pixel under test tends to be anomalous. If the score is close to 0, then the pixel under test is close to the background. Using this principle, we can identify the anomalous targets in the test scene. Result To verify the effectiveness of the proposed algorithm, the experimental part selects two scenes from the San Diego dataset and one scene from the ABU-Airport dataset and uses the traditional algorithms like global RXD (GRXD), local RXD (LRXD), and collaborative representation-based detector (CRD) and the anomaly detection algorithm based on convolutional neural network with pixel-pair feature (PPF-CNN) as the comparative algorithms. The receiver operating curve (ROC) of each algorithm is drawn, and the corresponding area under the ROC curve (AUC) value is calculated as an evaluation index of algorithm performance. In the anomaly detection experimental results of the three scenes, the proposed PPF-SNN has the highest AUC values of 0.993 51, 0.981 21, and 0.984 38, respectively. It can ensure the highest detection rate while keeping the false alarm rate low. The performance of PPF-SNN has obvious advantages over traditional algorithms and PPF-CNN algorithm. Conclusion The proposed hyperspectral image anomaly detection method based on siamese neural network can extract the deep spectral characteristics of the input pixel pair. According to the difference in its characteristics, the network will learn the distinction between the two. Thus, it can effectively provide the anomaly score of the pixel under test relative to the surrounding background. Compared with PPF-CNN, the proposed method can effectively reduce false alarms. It can also highlight anomalous targets more obviously, improve the detection rate, and exhibit stronger robustness than traditional methods.