Multispectral target detection based on the space-spectrum structure constraint with the multi-scale hierarchical model

Aimed at the interference of scene and the local overly split, we fully excavate the information of spatial and spectral domain to construct the feature descriptor called local and neighbor multiscale, and construct a hierarchical structure model for multispectral target detection. Based on multidimensional differential distance measure, we put forward Space and Spectrum Differential Structure (SSDS) operator to extract small scale fine structure and robust feature of multispectral targets. Based on the idea of binary patterns, we propose Local and Neighbor Binary Pattern (LNBP) operator for large scale neighborhood structure extraction of multispectral targets. Finally, we construct pyramid hierarchical structure model (MH-LS, Multi-scale Hierarchical-LS), design a multiscale and multilevel computing architecture, and fully unite the characterization and separability of two operators on the multidimensional multiscale structure. Also, MH-LS can make robust matching based on small sample coming true and improve the detection accuracy and efficiency. Experiments show that MH-LS does not need a large number of sample training, and can effectively detect multispectral objects in different scenes, postures, views and scales based on a small number of template sets.