Change detection is an essential and widely utilized task in remote sensing that aims to detect and analyze changes occurring in the same geographical area over time, which has broad applications in urban development, agricultural surveys, and land cover monitoring. Detecting changes in remote sensing images is a complex challenge due to various factors, including variations in image quality, noise, registration errors, illumination changes, complex landscapes, and spatial heterogeneity. In recent years, deep learning has emerged as a powerful tool for feature extraction and addressing these challenges. Its versatility has resulted in its widespread adoption for numerous image-processing tasks. This paper presents a comprehensive survey of significant advancements in change detection for remote sensing images over the past decade. We first introduce some preliminary knowledge for the change detection task, such as problem definition, datasets, evaluation metrics, and transformer basics, as well as provide a detailed taxonomy of existing algorithms from three different perspectives: algorithm granularity, supervision modes, and learning frameworks in the methodology section. This survey enables readers to gain systematic knowledge of change detection tasks from various angles. We then summarize the state-of-the-art performance on several dominant change detection datasets, providing insights into the strengths and limitations of existing algorithms. Based on our survey, some future research directions for change detection in remote sensing are well identified. This survey paper will shed some light on the community and inspire further research efforts in the change detection task.

翻译：变化检测是遥感领域中一项基础且广泛应用的任务，旨在检测和分析同一地理区域随时间发生的变化，在城市发展、农业调查和土地覆盖监测等领域具有广泛应用。由于图像质量差异、噪声、配准误差、光照变化、复杂景观和空间异质性等多种因素，遥感图像中的变化检测是一项复杂的挑战。近年来，深度学习作为一种强大的特征提取工具，在应对这些挑战方面崭露头角，其多功能性使其被广泛采用于众多图像处理任务。本文全面综述了过去十年遥感图像变化检测的重大进展。我们首先介绍了变化检测任务的一些基础知识，如问题定义、数据集、评估指标和Transformer基础，并在方法论部分从算法粒度、监督模式和学习框架三个不同角度对现有算法进行了详细分类。本综述使读者能够从多个角度系统掌握变化检测任务的知识。随后，我们总结了在几个主流变化检测数据集上的最优性能，提供了对现有算法优势和局限性的深刻见解。基于我们的综述，明确指出了遥感变化检测的若干未来研究方向。本综述论文将为相关研究领域提供启示，并激发变化检测任务中的进一步研究努力。