Unmanned aerial vehicles (UAVs) and birds are typical low-altitude small targets in optical remote sensing, often occupying only a few pixels and exhibiting highly similar appearances, which limits the effectiveness of appearance-based discrimination at long distances and low resolutions. To overcome this, we propose a non-appearance-based classification framework using kinematic and time–frequency features. At the trajectory level, kinematic features—including the coefficient of variation of velocity and acceleration, the Spatiotemporal Box-counting Fractal Dimension (SBFD), and the Local Higuchi Fractal Dimension (LHFD)—quantify multi-scale trajectory complexity. At the scale-variation level, time–frequency features, specifically the Time-Frequency Aware Singular Value Entropy (TF-SVE) derived from bounding-box area sequences, capture non-stationary oscillations from bird wing flapping, reflecting behavioral differences from rigid UAV motion. Experiments on a complex real-world dataset show that stacking these features achieves 99.47% classification accuracy, demonstrating a robust, resolution-invariant, and practically effective approach for non-appearance-based recognition of low-altitude targets.

Subpixel localization of Gaussian spot-like targets (e.g., stars, space objects) in electro-optical star images is critical for celestial positioning and spacecraft attitude measurement. This paper presents a comprehensive approach addressing the significant degradation of localization precision caused by outliers and non-Gaussian intensity distributions. The main novelties include: (1) a Gaussian PSF model with a bias correction term to resolve the Gaussian/non-Gaussian tension, effectively addressing systematic errors in small spot targets; (2) an iterative PSF estimation framework that progressively refines localization accuracy; and (3) a critical control point strategy that provides robustness against outliers through deterministic iterative refinement and dynamic hard selection, distinguishing it from conventional robust fitting approaches like IRLS (which uses continuous weighting) or RANSAC (which relies on random sampling). This method assumes a symmetric Gaussian PSF, specifically tailored for scenarios where this assumption holds (e.g., stare-mode celestial positioning, spacecraft attitude measurement with calibrated optics). While assuming a symmetric Gaussian PSF model, our method introduces a correction term to compensate for the non-Gaussian grayscale distribution arising from discrete sampling of small spot targets. Within each iteration, the framework selects appropriate pixel points as critical control points (CCPs) based on the current model estimation, and uses these selected points to construct a new least-squares problem to update the target’s PSF parameters. The critical control point strategy effectively excludes strong outliers from participating in solving the least-squares problem, thereby enhancing both noise robustness and localization accuracy. The method’s efficacy is validated through comprehensive simulations and experiments on real star image sequences. The method demonstrates effectiveness across various conditions, though limitations exist for severely undersampled targets, saturated pixels, anisotropic PSFs, and crowded star fields. Results indicate that the proposed method achieves competitive performance compared to existing approaches in terms of localization accuracy, noise robustness, and consistency across sequences.