Abstract. Ordinary multispectral (MS) image change detection (CD) techniques have not excavated the inherent information of MS images. Deep learning methods are a type of effective method that grabs the changed and unchanged information by deep feature extraction. However, a larger number of labeled samples are difficult to obtain for CD in MS images. An unsupervised MS image CD method is proposed based on a teacher student (TS) model. First, pseudotrain samples are produced by an expectation–maximization algorithm, and then the reliable training samples are selected based on the information of local windows. Second, the pseudotraining samples are used as input of the TS model, which is a self-ensembling model and includes a student model and a teacher model. Then the better teacher model of the TS model is updated by the exponential moving average of the student’s weight at each training step. Finally, the trained teacher model is utilized to generate the CD map. The experiments on four sets of bitemporal MS images demonstrate that the proposed CD method performs well in comprehensive indices compared with existing methods.

中文翻译：

---

基于师生模型的双时相多光谱图像变化检测

摘要。普通的多光谱 (MS) 图像变化检测 (CD) 技术并没有挖掘出 MS 图像的内在信息。深度学习方法是一种通过深度特征提取获取变化和不变信息的有效方法。然而，对于 MS 图像中的 CD，难以获得大量标记样本。提出了一种基于教师学生（TS）模型的无监督 MS 图像 CD 方法。首先，通过期望最大化算法产生伪训练样本，然后根据局部窗口的信息选择可靠的训练样本。其次，将伪训练样本用作 TS 模型的输入，该模型是一个自集成模型，包括学生模型和教师模型。然后在每个训练步骤通过学生权重的指数移动平均更新 TS 模型的更好的教师模型。最后，利用经过训练的教师模型生成 CD 地图。在四组双时态 MS 图像上的实验表明，与现有方法相比，所提出的 CD 方法在综合指标方面表现良好。