Change Detection in Remote Sensing Imagery: A Systematic Review of Statistical, Machine Learning, and Deep Learning Methods

Change detection (CD) is a fundamental remote sensing task that identifies surface modifications from multi-temporal imagery of the same area, with applications in urban monitoring, agriculture, forest disturbance mapping, disaster assessment, and land cover analysis. The task is complicated by radiometric and atmospheric variability, co-registration errors, seasonal cycles, and sensor heterogeneity. Deep learning has progressively superseded traditional and classical machine learning approaches through hierarchical feature extraction and end-to-end optimization. Following the PRISMA 2020 guidelines, this systematic review examines 144 primary studies identified through a structured Scopus search complemented by the authors’ prior research and citation searching, spanning three paradigms: traditional approaches (algebraic operators, transformations, probabilistic frameworks), classical machine learning (support vector machines, random forests, object-based analysis), and deep learning architectures (fully convolutional, Siamese, attention-based, Transformer, state space, diffusion-based, and weakly supervised models). We provide background on problem formulation, benchmark datasets, and evaluation metrics, alongside a taxonomy organized by paradigm and supervision mode. A quantitative comparison on dominant benchmarks reveals the strengths and limitations of current methods. Open challenges include the absence of a universal benchmark protocol, the research-to-deployment gap, and the need for label-efficient learning. This review serves as a structured reference and outlines promising directions for the field.