Understanding the temporal dynamics of Earth's surface is a mission of multi-temporal remote sensing image analysis, significantly promoted by deep vision models with its fuel -- labeled multi-temporal images. However, collecting, preprocessing, and annotating multi-temporal remote sensing images at scale is non-trivial since it is expensive and knowledge-intensive. In this paper, we present a scalable multi-temporal remote sensing change data generator via generative modeling, which is cheap and automatic, alleviating these problems. Our main idea is to simulate a stochastic change process over time. We consider the stochastic change process as a probabilistic semantic state transition, namely generative probabilistic change model (GPCM), which decouples the complex simulation problem into two more trackable sub-problems, \ie, change event simulation and semantic change synthesis. To solve these two problems, we present the change generator (Changen), a GAN-based GPCM, enabling controllable object change data generation, including customizable object property, and change event. The extensive experiments suggest that our Changen has superior generation capability, and the change detectors with Changen pre-training exhibit excellent transferability to real-world change datasets.

翻译：理解地球表面的时态动态是多时相遥感图像分析的核心任务，而深度视觉模型在该任务上的显著进展离不开其燃料——标注的多时相图像。然而，规模化收集、预处理和标注多时相遥感图像并非易事，因其成本高昂且知识密集。本文提出一种基于生成式建模的可扩展多时相遥感变化数据生成器，该生成器低成本且全自动，可缓解上述问题。我们的核心思路是模拟随时间变化的随机变化过程。将随机变化过程视为概率语义状态转移，即生成式概率变化模型（GPCM），该模型将复杂模拟问题解耦为两个更易处理的子问题：变化事件模拟与语义变化合成。为解决这两个问题，我们提出基于GAN的GPCM——变化生成器（Changen），实现可控目标变化数据生成，包括可自定义目标属性与变化事件。大量实验表明，我们的Changen具有卓越的生成能力，且经Changen预训练的变化检测器在真实变化数据集上展现出优异的迁移性。