基于相似度的目标可见光谱段伪装效能评估
Research on the Evaluation of Target Camouflage Effect in Visible Spectrum Based on Similarity
时萌玮 1闫钧华 1徐国跃 2张寅 1胡旭彤 1钱淇2
作者信息
- 1. 南京航空航天大学空间光电探测与感知工业和信息化部重点实验室,江苏 南京 211106
- 2. 南京航空航天大学材料科学与技术学院,江苏 南京 211106
- 折叠
摘要
伪装目标与背景的综合相似度能够衡量伪装目标与周围背景之间的特征差异,综合相似度越高,目标与背景的融合程度越高,目标伪装效能越高.组合使用目标及背景图像的亮度、颜色、纹理、形状、尺寸、结构、直方图等7个典型特征来表征目标伪装特性,采用与其相应的相似度量法得到各个特征相似度,采用熵权法客观确定不同特征指标权重,线性加权得到伪装目标与背景的综合相似度,从而获得量化的目标伪装效能评估结果.不同背景、不同侦察距离、不同目标姿态下的多组实验表明:所提伪装目标与背景的综合相似度具有与目标发现概率一致性、与探测距离一致性、背景鲁棒性,证明了所提方法的正确性和有效性;并通过对比实验验证了该方法的可靠性.
Abstract
The comprehensive similarity between the camouflage target and the background can measure the feature difference between the camouflage target and the surrounding background.The higher the comprehensive similarity,the higher the fusion degree between the target and the background,and the higher the camouflage efficiency of the target.In this paper,seven typical features of target and background images,including brightness,color,texture,shape,size,structure,and histogram,are used to represent the camouflage characteristics of the target.The corresponding similarity measurement method is used to obtain the similarity of each feature.The entropy weight method is used to objectively determine the weights of different feature indicators,and the comprehensive similarity between the camouflage target and background is obtained by linear weighting.Thus,the quantitative evaluation results of target camouflage effectiveness can be obtained.Multiple experiments under different backgrounds,different reconnaissance distances and different target attitudes show that the comprehensive similarity between the camouflage target and the background proposed in this paper is consistent with the target discovery probability,consistent with the detection distance,and robust to the background,which proves the correctness and effectiveness of the proposed method.The reliability of the proposed method is proved by comparison experiment.
关键词
伪装效能评估/综合相似度/可见光谱段/伪装目标/熵权法Key words
camouflage effectiveness evaluation/comprehensive similarity/visible light/camouflage target/entropy method引用本文复制引用
出版年
2024
NETL
NSTL
万方数据