Koopman-Based Transition Detection in Satellite Imagery: Unveiling Construction Phase Dynamics Through Material Histogram Analysis

In terms of monitoring and managing anthropogenic activities, accurately identifying the distinct phases in construction projects using satellite imagery remains a challenging task. In this paper, we reformulate the phase classification problem into a transition detection problem and introduce a novel Koopman-based Transition Detection (KTD) method, which applies Koopman operator theory to analyze the nonlinear dynamics of material histograms in a linear framework. KTD employs a sliding window to perform Dynamic Mode Decomposition (DMD) on the time-series material histograms and detects the transition point by analyzing the movement of the eigenvalue in consecutive strides. Compared to CNN-based methods, our proposed KTD method demonstrates enhanced accuracy and reduced temporal error in phase identification. Furthermore, as an unsupervised method that does not require large amounts of training data, it shows a better generalization capability in the sequestered region.