Change detection is a long-standing and challenging problem in remote sensing. Very often, features about changes are difficult to model beforehand, thus making the collection of changed samples a challenging task. In comparison, it is much easier to collect numerous no-change samples. It is possible to define a change detection approach using only easily available annotated no-change samples, which we henceforth call one-class change detection. Autoencoder networks being trained on no-change data are natural candidates for addressing this task due to their superior performance when compared with other one-class classification models. However, the autoencoders usually suffer from the problem of overgeneralization, i.e., they tend to generalize too well, thus risking properly reconstructing changed samples. In this article, we propose a novel data-enclosing-ball minimizing autoencoder (DebM-AE) that is trained with dual objectives—a reconstruction error criterion and a minimum volume criterion. The network learns a compact latent space, where encodings of no-change samples have low intraclass variance, which as counterpart has the identification of changed instances. We conducted extensive experiments on three real-world datasets. Results demonstrate advantages of the proposed method over other competitors. We make our data and code publicly available ( https://gitlab.lrz.de/ai4eo/reasoning/DebM-AE; https://github.com/lcmou/DebM-AE ).

中文翻译：

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通过学习无变化来检测变化：用于多光谱图像中一类变化检测的数据封闭球最小化自动编码器

变化检测是遥感中长期存在且具有挑战性的问题。很多时候，关于变化的特征很难事先建模，因此收集变化的样本是一项具有挑战性的任务。相比之下，收集大量不变样本要容易得多。可以仅使用容易获得的带注释的无变化样本来定义变化检测方法，我们此后将其称为一类变化检测。与其他一类分类模型相比，在无变化数据上训练的自动编码器网络自然成为解决此任务的候选者，因为它们具有卓越的性能。然而，自编码器通常会遇到过度泛化的问题，即它们往往泛化得很好，从而冒着正确重建变化样本的风险。在本文中，我们提出了一种新颖的数据封闭球最小化自动编码器（DebM-AE），该编码器具有双重目标——重建误差标准和最小体积标准。网络学习一个紧凑的潜在空间，其中不变样本的编码具有较低的类内方差，作为对应物具有变化实例的识别。我们对三个真实世界的数据集进行了广泛的实验。结果证明了所提出的方法优于其他竞争对手的优势。我们公开我们的数据和代码（作为对应方具有更改实例的标识。我们对三个真实世界的数据集进行了广泛的实验。结果证明了所提出的方法优于其他竞争对手的优势。我们公开我们的数据和代码（作为对应方具有更改实例的标识。我们对三个真实世界的数据集进行了广泛的实验。结果证明了所提出的方法优于其他竞争对手的优势。我们公开我们的数据和代码（ https://gitlab.lrz.de/ai4eo/reasoning/DebM-AE； https://github.com/lcmou/DebM-AE ）。