Unsupervised Occluded Target Detection Based on Spherical Shell With Multispectral Point Clouds

Multispectral point clouds (MPCs) acquired from uncrewed aerial vehicles (UAVs), leveraging LiDAR’s canopy-penetrating capacity, provide distinct advantages for detecting occluded targets beneath vegetation canopy. However, limited samples, missing target spatial morphology, and unstructured data format have led to low occluded detection accuracy. Given these constraints, an unsupervised stereo detection method for occluded targets based on the spherical shell model with MPCs from UAVs has been proposed for the first time. The method exploits the spatial-spectral differences between targets and the background, treating targets as anomalies within the background, and enables occluded target detection without requiring training. The spherical shell model has been constructed in MPCs to avoid contaminating the global background with targets, leveraging its local separation characteristics for unsupervised detection of occluded targets. An adaptive radius and multiscale spatial feature extraction have been designed to enhance the method’s robustness. The collaborative representation model has been utilized to achieve unsupervised detection by suppressing expressible points in local background features. To bridge the gap between algorithmic metrics and practical requirements, we have further proposed a target-level evaluation method that overcomes the limitations of conventional methods, which are susceptible to density-induced false alarm distortions in unstructured MPCs. Experiments on three real-world MPCs and a public airborne hyperspectral and LiDAR dataset show that our method achieves higher detection accuracy than existing spectral-based detectors and performs better on exposed targets.