基于光谱成像系统的小目标识别技术的研究
Research on Small Object Recognition Based on Spectral Imaging System
DOI: 10.12677/CSA.2020.105110, PDF,   
作者: 管泽海*, 李 野:长春理工大学,吉林 长春;付明艳:63850部队,吉林 白城
关键词: 光谱成像目标识别图像算法Spectral Imaging Target Recognition Image Algorithm
摘要: 针对天地复杂背景下红外弱小目标的检测,提出了一种基于光谱成像融合top-hat变换、光谱识别的新方法。首先将改进的top-hat变换应用于红外弱小目标图像,将图像中与目标特性相似的像素进行增强,并去除云层背景对目标的干扰,得到若干疑似目标的增强结果,并结合阈值分割方法对增强结果进行筛选,剔除大部分不符合弱小红外目标特性的背景干扰点。然后采用光谱匹配算法,通过三种测度进行加权匹配,去除图像处理中对干扰物、噪声等因素的干扰,得到筛选过的目标识别结果,通过实验证明,本文提出的算法相较于传统弱小目标检测有更好的检测效果。
Abstract: We proposed a new method based on spectral imaging fusion top-hat transform and spectral recognition for the detection of infrared dim and small targets in the complex background of earth and sky. First this paper applies the improved top-hat transform infrared dimsmall target image, similar to the target characteristics of the pixel in the image enhancement, and removes the interference of background on the target, the clouds get some suspected targets enhanced as a result, and combining the threshold segmentation method to enhance to filter results, eliminating most do not conform to the small and weak infrared target characteristics of the background noise points. Then, the spectral matching algorithm is adopted to make weighted matching through three measures to remove the interference of distractor and noise and other factors in the image processing, and the filtered target recognition results are obtained. The experimental results show that the algorithm proposed in this paper has a better detection effect than the traditional weak and small target detection.
文章引用:管泽海, 李野, 付明艳. 基于光谱成像系统的小目标识别技术的研究[J]. 计算机科学与应用, 2020, 10(5): 1057-1063. https://doi.org/10.12677/CSA.2020.105110

参考文献

[1] Deshpande, S.D., Meng, H.E., Venkateswarlu, R., et al. (1999) Max-Mean and Max-Median Filters for Detection of Small Targets. SPIE’s International Symposium on Optical Science, Engineering, and Instrumentation, International So-ciety for Optics and Photonics, 3809, 74-83. [Google Scholar] [CrossRef
[2] 卞山峰, 张庆辉. 基于改进YOLO v2的车辆实时检测算法[J]. 电子质量, 2019(10): 19-22.
[3] 刘源, 汤心溢, 李争. 基于新 Top-hat 变换局部对比度的红外小目标检测[J]. 红外技术, 2015, 37(7): 544-552.
[4] 王建永, 范小虎, 赵爱罡. 基于方向梯度的红外小目标检测算法[J]. 无线电工程, 2018, 48(12): 1077-1080.
[5] 徐文晴, 王敏. 基于自适应形态学滤波的红外小目标检测算法[J]. 激光与红外, 2017, 47(1): 108-113.
[6] 陈志学, 罗蓓蓓, 孔鹏, 等. 红外搜索系统中弱小目标检测算法研究[J]. 应用光学, 2011, 32(5): 987-991.
[7] 林丰辉. 海天背景下红外目标的检测与识别研究[J]. 舰船电子工程, 2019, 39(1): 147-151.
[8] 崔屹. 图像处理与分析: 数学形态学方法及应用[M]. 北京: 科学出版社, 2000: 15-28.
[9] 邹江威, 陈曾平. 应用形态学与图像流法的空间小目标提取方法[J]. 光电工程, 2005, 32(4): 13-15.
[10] 顾宪松, 高昆, 朱振宇, 张鑫, 韩璐. 多源红外弱小目标灰色关联融合识别方法[J]. 激光与红外, 2018, 48(10): 1258-1263.
[11] 姚迅, 李德华, 孙贤斌, 等. 一种多阶段处理的红外小目标检测方法[J]. 武汉理工大学学报(交通科学与工程版), 2008, 32(6): 1141-1144.
[12] 董维科. 基于可分离特征的红外弱小目标检测方法研究[M]. 西安: 西安电子科技大学, 2011.
[13] Zhang, S.K., Cai, J. and Yang, Y.J. (2012) Simulation of Infra-red Radiation Characteristics of the Exhaust Plume by Using Backward Monte Carlo Method. Infrared and Laser Engi-neering, 41, 2604-2609.
[14] Liu, Z.Y., Shao, L., Wang, Y.F., et al. (2013) Influence of Flight Patameters in the Infra-red Radiatioin of a Lipuid Racket Exhaust Plume. Acta Optica Sinica, 33, Article ID: 0404001. [Google Scholar] [CrossRef
[15] 瞿芳芳, 任东, 侯金健, 等. 基于向前和向后间隔偏最小二乘的特征光谱选择方法[J]. 光谱学与光谱分析, 2016, 36(2): 593-598.
[16] 汤晓君, 郝惠敏, 李玉军, 等. 基于Tikhonov正则化特征光谱选择与最优网络参数选择的轻烷烃气体分析[J]. 光谱学与光谱分析, 2011, 31(6): 1673-1677.
[17] 苑智玮, 黄树彩, 熊志刚, 等. 尾焰特征光谱在主动段弹道目标识别中的应用[J]. 光学学报, 2017, 37(2): 023001-1-023001-8.