基于光照模糊相似融合不变矩的航天器目标识别

针对航天器目标识别中现有的光照变化和图像模糊条件下几何仿射不变矩识别不稳健问题，根据灰度光照模型，结合图像模糊和不变矩的基本理论，提出了一种光照模糊不变量，与几何仿射不变矩进行融合，构建出一种新的光照模糊相似融合（CIBA）不变矩，并从理论上证明了光照、模糊和相似变换（平移、缩放和旋转）不变性。对3种不同的航天器目标的识别实验表明，光照模糊相似融合不变矩在最小距离分类器中的识别准确率达到了86.37%，比几何仿射不变矩提高了94.00%，比具有光照不变性的几何仿射不变矩提高了24.54%，有效地解决了对不同位姿、不同光照模糊条件下的航天器目标识别问题，提高了基于矩的目标识别鲁棒性。

CIBA Moment Invariants and Their Use in Spacecraft Recognition Algorithm

In order to solve the non-robustness for affine moment invariants with changing illumination conditions and unknown blurring, an illumination and blur invariant is proposed which is based on the gray light model, image blurring theory and basic moment invariants theory. Combining the geometric affine moment invariants with the proposed illumination and blur invariants, the combined illumination, blur and affine (CIBA) moment invariants are built and their invariant features of illumination, blur and similarity transform (i.e. rotation, scaling and translation) are theoretically proved. Taking multiple images of three spacecraft models as examples, the average recognition accuracy by using CIBA moment invariants in a linear minor distance classifier can reach 86.37%, which is a 94.00% improvement compared with the affine moment invariants, and a 24.54% improvement as compared with the illumination moment invariants. CIBA moment invariants can effectively solve the problem in the identification of spacecraft under different conditions of illumination, unknown blurring and pose. They enhance the target recognition robustness which is based on moment invariants.