Satellite Video Event of Interest Detection Using Deep Spatiotemporal Metric

The rapid advancements in video satellite technology have enabled dynamic Earth observation. However, the unique characteristics of satellite video data, such as extensive spatial coverage, sparse motion information, and significant foreground–background imbalance, introduce numerous challenges for practical applications. To address these difficulties, the task of event of interest (EOI) detection has emerged as a critical solution by extracting meaningful regions containing dynamic information from satellite videos. In this article, a novel deep learning framework for EOI detection, integrating spatiotemporal slice analysis and metric learning to tackle these challenges is proposed. The framework employs spatiotemporal slice analysis to convert 3-D motion information into 2-D motion trajectories, preserving essential dynamic information while minimizing background redundancy. A ResNet-based network is adopted for robust feature extraction, and a custom Earth mover’s distance (EMD) metric is introduced to enhance detection precision, enabling accurate differentiation between EOIs and non-EOIs in satellite video data. Experimental results demonstrate the superior performance of the proposed method.