Abstract

We address the crucial task of identifying changes in land cover using remotely sensed imagery. While most change detection methods focus on two images, we introduce an unsupervised approach that considers long image series (more than two), supporting a more nuanced differentiation between changed and unchanged areas. The proposed technique transforms input data to a new representation, capturing the target's spectral response changes over time. Areas with minimal response variation are identified as non-changing and distinguished from regions that have undergone modifications. The method further categorizes, utilizing statistical procedures, regions undergoing spatiotemporal modifications into seasonal or permanent changes. Experimental validation using simulated and real-world remote sensing image series demonstrates the effectiveness of the proposed approach.