Unsupervised Multitemporal Triclass Change Detection

Author(s)

Negri, Rogerio G. ; Frery, Alejandro C. ; Casaca, Wallace ; Gamba, Paolo ; Bhattacharya, Avik

• Document type: Journal article
• Source: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING; v. 62, p. 17-pg., 2024-01-01.

Abstract

Change detection is a fundamental task that involves assessing changes in a given region over multiple time periods. It has been widely applied across various fields, including monitoring deforestation, urban expansion, and natural disaster analysis. In this article, we address the critical and complex issue of automatically identifying types of changes in land cover using remotely sensed imagery. While conventional unsupervised change detection methods typically focus on comparing pairs of images and making a binary decision between "change" and "nonchange," our approach tackles the challenge of analyzing long image series and identifying the kind of change. Under this condition, the unsupervised change detection process allows for a more informative identification of the land cover dynamics. Moreover, our approach transforms input data to a new representation, capturing the target's spectral response changes over time. Through the utilization of stochastic distances and an optimized thresholding scheme, areas exhibiting minimal spectral response variance are classified as unchanged, effectively distinguishing them from regions undergoing modifications. Next, by applying autocorrelation analysis, regions exhibiting temporal modifications are segregated into periodic (i.e., seasonal) and aperiodic (i.e., permanent) change cases. Experimental validation using both simulated and real-world remote sensing image series demonstrates the effectiveness of the proposed approach. (AU)

• FAPESP's process: 21/01305-6 - Theoretical advances on anomaly detection and environmental monitoring systems building
• Grantee: Rogério Galante Negri
• Support Opportunities: Regular Research Grants
• FAPESP's process: 23/02538-0 - Time series, wavelets, high dimensional data and applications
• Grantee: Aluísio de Souza Pinheiro
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 21/03328-3 - Development of new methodologies and machine intelligence-based technological solutions for digital image segmentation and COVID-19 pandemic response
• Grantee: Wallace Correa de Oliveira Casaca
• Support Opportunities: Regular Research Grants