基于光谱的天基空间点目标特征提取与识别

天基空间目标观测时, 对于远距离目标, 通常只能得到一个点的相关信息, 包括目标的位置和灰度等, 损失了目标的材料、大小和状态等特征. 代表物体固有属性差异的光谱特性可作为目标特征提取与识别的一种重要手段. 从目标光谱特性的产生特点出发, 综合考虑目标的材料特性、结构特性、背景特性、轨道特性等因素, 建立了目标光谱特性的数学模型, 提出了基于光谱特性数学模型反演计算的目标特征提取与识别方法, 以环境一号卫星缩比模型为例, 进行了典型参数条件下的目标特征提取与识别实验验证, 实验结果验证了建模方法的正确性.

Feature extraction and recognition of non-resolved space ob ject from space-based sp ectral data

The location and intensity of non-resolved space object can be obtained in space-based surveillance, however, the material, size, and status of the object are lost. Spectrum represents the inherent property difference of the object, which can be used as an important means for feature extraction and recognition of non-resolved space object. According to the influence factors, including material, structure, background and orbit, a mathematical model for spectral properties of space object is established. Based on the model, inverse calculation method for feature extraction and recognition of space object is proposed. Taking the HuanJing-1 satellite scale model as an example, experimental verification for feature extraction and recognition of space object in typical parameters is made. Experimental results demonstrate the validity of the modeling method.