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

Fellipe Mira Chaves, Rogério Galante Negri, Larissa Mioni Vieira Alves, Adriano Bressane (), Aliihsan Sekertekin, Erivaldo Antônio da Silva, Guilherme Pina Cardim and Wallace Casaca
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Fellipe Mira Chaves: Science and Technology Institute, São Paulo State University (UNESP), São José dos Campos 12245-000, Brazil
Rogério Galante Negri: Science and Technology Institute, São Paulo State University (UNESP), São José dos Campos 12245-000, Brazil
Larissa Mioni Vieira Alves: Science and Technology Institute, São Paulo State University (UNESP), São José dos Campos 12245-000, Brazil
Adriano Bressane: Graduate Program in Civil and Environmental Engineering, São Paulo State University (UNESP), Bauru 17033-360, Brazil
Aliihsan Sekertekin: Vocational School of Technical Sciences, Igdir University, 76000 Igdir, Türkiye
Erivaldo Antônio da Silva: Faculty of Science and Technology, São Paulo State University (UNESP), Presidente Prudente 19060-900, Brazil
Guilherme Pina Cardim: School of Engineering and Sciences, São Paulo State University (UNESP), Rosana 19272-100, Brazil
Wallace Casaca: Institute of Biosciences, Humanities and Exact Sciences, São Paulo State University (UNESP), São José do Rio Preto 15054-000, Brazil

Sustainability, 2025, vol. 17, issue 21, 1-22

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.

Keywords: change detection; time series; spectral indices; unsupervised; machine learning; pseudo-label (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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