Unsupervised Change Detection Approach via Pseudo-Labeling, Machine Learning, and Spectral Index Time Series

Abstract

Land-use and land-cover change detection is critical for monitoring deforestation and urban expansion. In this study, we propose an unsupervised change detection approach that leverages multi-temporal satellite imagery combined with a classic machine learning algorithm trained on automatically generated pseudo-labels. Four distinct study areas were analyzed: a tropical forest region in the Brazilian Amazon, an agricultural frontier in the Amazon, a Brazilian Savanna area undergoing transformation, and a rapidly expanding urban zone around the new Istanbul Airport, in Türkiye. The performance of the proposed approach was evaluated and compared with modern unsupervised change detection methods, including the Wavelet Energy Correlation Screening and the Temporal Convolutional Autoencoder methods. The results demonstrate that the proposed framework achieved consistently high accuracy across all four study areas, with F1-scores of approximately 0.92 in dense forest, 0.87 in an agricultural frontier, 0.91 in the savanna area, and 0.89 in an urban expansion zone. Overall, the model outperformed or matched the performance of the baseline methods, attesting to its adaptability and generalization capability in diverse environmental contexts worldwide.