Spectral-Temporal Attention for Robust Change Detection

Abstract

Change detection has long been used for various tasks. With advancements in robotic systems and computer vision, change detection techniques can be further explored for diverse applications. Current state-of-the-art methods primarily use either satellite images or street-level images to detect changes. However, the techniques used for these two types of images differ substantially, though their core objective remains identical.We introduce a spectral-temporal attention network capable of detecting changes in both satellite and street-level images across multiple temporal instances. Additionally, we present an indoor environmental dataset featuring significantly more frequent changes. We analyze the impact of temporal and spatial domain shifts on the performance of various methods and demonstrate that performing attention in the spectral domain not only enhances overall performance but also increases robustness against spatial domain shifts.