  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.
