Abstract
© 2025 The AuthorsSynthetic Aperture Radar (SAR) is used in a wide variety of fields, such as monitoring failures and measuring infrastructure health. Detecting spatio-temporal changes in the observed scene is of paramount importance, particularly considering the prevention of hazards. In this paper, we propose a novel nonparametric method called Band-limited Uncalibrated Detector (BUD) for change detection using InSAR coherence. BUD is a flexible, robust, and responsive tool designed for monitoring applications. It directly inspects observed data, making inferences without relying on strong theoretical assumptions or requiring calibration with known stable targets. It achieves this by applying a nonparametric statistical hypothesis test to multi-temporal InSAR coherence samples, specifically looking for differences in their statistical distributions. After outlining the theoretical principles of our proposed algorithm, we present a synthetic performance analysis comparing BUD with various state-of-the-art methods. Then, BUD is applied to two challenging real-world scenarios crucial for monitoring applications: an open-pit mining site, known for frequent and composite environmental changes, and an urban area, which typically experiences infrequent changes demanding highly responsive change detection methods. In both cases, we provide a comparison with other leading methods. Finally, we cross-validate BUD in the open-pit mine scenario by intersecting analysis results from three different InSAR datasets covering the same area of interest, featuring diverse acquisition geometries and operational bandwidths (X-Band and C-Band), proposing a novel way to interpret InSAR data. The algorithm's final validation is achieved using available ground truth data in the urban scenario.
NETL
NSTL
Elsevier