A “Difference-in-Differences”-Based Method for Unsupervised Change Detection in Season-Varying Images

Unsupervised change detection in season-varying images remains challenging due to the pseudo-changes induced by the seasonal variation of the images acquired at different times. Although various transformations [e.g., multivariate alteration detection (MAD), slow feature analysis (SFAs), etc.] have been developed to alleviate this issue, the complicated seasonal variation in a heterogenous landscape cannot be well addressed. This study developed a novel “difference-in-differences”-based change detection (DIDCD) method, which calculates the change magnitude by performing two difference operations between the changes observed in the target pixel and those in the control group. The control group is established by a combined method of k-means clustering and minimum covariance determinant (MCD), which identifies similar pixels that undergo parallel seasonal changes compared to the target pixels. Through DID operation, DIDCD eliminates seasonal variation and accurately identifies land cover change. DIDCD is evaluated using four pairs of images under both season-consistent and season-varying conditions across two scenarios (urban expansion and forest fire disturbance). The results demonstrate that DIDCD effectively suppresses the pseudo-changes induced by the seasonal variation and outperforms the existing unsupervised change detection algorithms.