Onboard Anomaly Detection for Marine Environmental Protection

Abstract

This article describes a comprehensive artificial intelligence pipeline for detecting threatening events in the marine environment, which is intended to be run on-board Earth observation satellites and globally contribute to the preservation of the marine environment. We employed a self-supervised neural network-based anomaly detection technique to identify a wide range of potentially unknown events (pollutions). This method consists of identifying deviations from the learned “normal” water state as abnormal events. Then, we can better characterize some of the anomalies by incorporating other, more selective models into our processing pipeline to prioritize targeted actions over specific anomalies (e.g., oil spills). This approach's effectiveness and versatility are demonstrated through its selection in two European Space Agency competitive challenges aimed at integrating artificial intelligence into two Earth observation demonstration missions: $\Phi$sat-2 and IMAGIN-e. The article details the processing pipeline design, the generated datasets for training and testing steps of the two challenges, and the detection effectiveness and hardware efficiency (including throughput and latency) when embedded on mission-representative hardware. Our primary contribution is the proposal of an operational pipeline with the constraints to 1) detect multiple and unknown events, 2) require only “weakly” labeled data, and 3) be computationally efficient enough to run on low-power targets on-board Earth observation payloads. These three assets make this pipeline easily adaptable to a wide range of missions.

Keywords

• Anomaly detection
• embedded artificial intelligence
• frugal learning
• marine environmental monitoring
• remote sensing