摘要
红外/被动毫米波(IR/PMMW)复合制导是当前发展多模复合制导技术的热点方向。红外探测系统在低能见度条件下的穿透能力不如被动毫米波,而被动毫米波探测图像分辨率不如红外图像。为了更好地识别目标的轮廓信息,提出一种新的基于小波包边缘检测的特征级主成分融合方法。新方法先用小波包边缘检测方法检测出包含水平边缘、垂直边缘和对角边缘的边缘图像,然后对边缘图像进行小波包去噪,再用主成分融合方法进行图像特征级的融合,最后用阈值方法提取出融合后的边缘。实验仿真结果表明,与传统的小波及小波包边缘检测方法相比,新方法融合后的边缘图像更容易分辨出目标的轮廓信息。
Infrared/millimeter wave （IR/MMW） composite guidance is currently a hot research topic in the field of multi-mode composite guidance technology. The penetrability of infrared detection system is less than that of passive millimeter wave under low visibility condition, but the image resolution of the passive millimeter wave （PMMW） detection is better than that of infrared images. In order to better identify the target contour information, in this paper, a new edge feature fusion method was put forward based on wavelet packet analysis and principal component analysis. The new method, firstly, uses wavelet packet edge detection to get three edge feature images which contain horizontal, vertical and diagonal edge feature respectively. Secondly, the edge feature images are de-noised using wavelet packet de-noising algorithm. Thirdly, the edge feature images are fused with principal component analysis method. Lastly, it is to get the fusion edge feature image with threshold method. The experimental results indicate that compared with traditional edge detections of wavelet analysis and wavelet packet analysis, the new fusion method can better recognize the contour information of the target object.
出处
《半导体光电》
CAS
北大核心
2015年第2期327-330,334,共5页
Semiconductor Optoelectronics
关键词
小波包
被动毫米波图像
主成分分析
边缘特征
评价指标
wavelet packet
PMMW image
principal component analysis
edge feature
evaluation index
