Toward a Novel Method for General On-Orbit Earth Surface Anomaly Detection Leveraging Large Vision Models and Lightweight Priors

Early warning systems and emergency management for disasters, environmental pollution, and illegal development require timely and accurate Earth surface anomaly detection (ESAD). Remote sensing, which uses satellites to observe the Earth’s surface, is an emerging approach to address this need. However, current remote sensing methods for ESAD are limited by their focus on specific anomalies, reliance on high-level satellite data, and the demand for significant computational and storage resources. In this article, we present a novel framework for general and on-orbit ESAD, which combines large vision models and lightweight priors. Our method characterizes images with a large vision model that is highly generalizable, reducing the dependency on high-level data, and compressing the prior base with sampling techniques, facilitating transmission and on-orbit storage. For on-orbit detection, we use a dictionary look-up style method for efficient anomaly prediction, enabling detection on satellites with limited computation resources. We evaluate our framework on typical scenarios and compare it with popular change detection (CD)-based and anomaly detection (AD)-based methods. Our results show that our framework achieves good performance while reducing the prior size by at least 95.56 times. Moreover, our framework can handle unpaired data, providing a chance to detect anomalies in the absence of near-term and paired images. Our framework has the potential to support the development and applications of general, on-orbit ESAD. The code and dataset are available at the following site:
https://github.com/YummyWaffle/ESAD
.