A Spatio–Spectral–Temporal Progressive Algorithm for Infrared Tiny Target Detection in Cluttered Scenes

Infrared tiny target detection is of great value in fields such as military reconnaissance and security early warning, but faces challenges including low signal-to-noise ratio (SNR), performance-efficiency tradeoffs, and detection-false alarm compromises in complex dynamic scenarios. To solve these questions, we propose a novel spatio–spectral–temporal progressive (SSTP) algorithm, integrating spatial, spectral, and temporal features for infrared tiny target detection in cluttered scenes. First, it adopts an anisotropic gradient difference detection algorithm to construct a spatial candidate target set based on the anisotropic radiation characteristics of target neighborhoods. Then, we use the isolation penalty adaptive clustering algorithm to obtain boundaries via outlier-enhanced clustering, and design a multilateral context filling algorithm to generate suspected regions and fill internal boundary information. In addition, we develop an adaptive nonlinear geometric filter for point screening using nonlinear structural features, apply a multiscale wavelet energy filter to capture high-frequency features, and utilize a target-background local difference measurement algorithm to extract regional independence for screening. Based on the proposed single-frame detection method, a multidimensional feature fusion-based dynamic target tracking algorithm is employed to extract moving targets. Experiments show that on multiframe datasets DSAT and single-frame datasets SIRST, the proposed method significantly outperforms mainstream algorithms, achieving detection rates of 98.75% and 98.23% as well as false alarm rates of 2.56 × 10^-6 and 10.86 × 10^-6, respectively. The algorithm not only performs well in multiframe detection, but also has good performance in single-frame detection. It thus provides a solution with high robustness and real-time performance for infrared early-warning systems in complex environments.