Adaptive Differential Event Detection for Space-Based Infrared Aerial Targets

Space resources are of economic and strategic value. Infrared (IR) remote sensing, unaffected by geography and weather, is widely used in weather forecasting and defense. However, detecting small IR targets is challenging due to their small size and low signal-to-noise ratio, and the resulting low detection rates (DRs) and high false alarm rates (FRs). Existing algorithms struggle with complex backgrounds and clutter interference. This paper proposes an adaptive differential event detection method for space-based aerial target observation, tailored to the characteristics of target motion. The proposed IR differential event detection mechanism uses trigger rate feedback to dynamically adjust thresholds for strong, dynamic radiation backgrounds. To accurately extract targets from event frames, a lightweight target detection network is designed, incorporating an Event Conversion and Temporal Enhancement (ECTE) block, a Spatial-Frequency Domain Fusion (SFDF) block, and a Joint Spatial-Channel Attention (JSCA) block. Extensive experiments on simulated and real datasets demonstrate that the method outperforms state-of-the-art algorithms. To advance research on IR event frames, this paper introduces SITP-QLEF, the first remote-sensing IR event dataset designed for dim and moving target detection. The algorithm achieves an [email protected] of 96.3%, an FR of 4.3 ×10−5, and a DR of 97.5% on the SITP-QLEF dataset, proving the feasibility of event detection for small targets in strong background scenarios.