• Infrared and Laser Engineering
  • Vol. 47, Issue 5, 526003 (2018)
Tong Xuanyue1、*, Wu Ran2, Yang Xinfeng2, Teng Shuhua3, and Zhuang Zhiyun3
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • 3[in Chinese]
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    DOI: 10.3788/irla201847.0526003 Cite this Article
    Tong Xuanyue, Wu Ran, Yang Xinfeng, Teng Shuhua, Zhuang Zhiyun. Fusion target recognition method of infrared and laser[J]. Infrared and Laser Engineering, 2018, 47(5): 526003 Copy Citation Text show less
    References

    [1] Yin Xiaochen, Fu Yanhui. Optical design of common aperture IR/ladar dual-mode imaging seeker [J]. Infrared and Laser Engineering, 2015, 44(2): 428-431. (in Chinese)

    [2] Wu Jiajie. Active-passive detection image registration and fusion[D]. Harbin: Harbin Institute of Technology, 2013. (in Chinese)

    [3] Fan Youchen, Zhao Hongli, Sun Huayan, et al. Calculation of maximum range of active and passive laser rangegated detection system[J]. Infrared and Laser Engineering, 2015, 44(S1): 86-92. (in Chinese)

    [4] Harney R C. Dual active/passive infrared imaging systems[J]. Optical Engineering, 1981, 20(6): 206976.

    [6] Barenz Joachim, Rainer Baumann, Frank Imkenberg, et al. All solid state imaging infrared/imaging ladar sensor system[C]//SPIE, 2004: 171-179.

    [7] DeFlumere Michael E. Reentry Vehicle Interceptor with IR and variable FOV laser radar: US, Google Patents[P]. 2004.

    [8] Liu LiPing, Sun Xiudong, Zhao Yuan, et al. Design of high-efficiency beam dividing system used in active-passive composite imaging radar[J]. Acta Optica Sinica, 2009, 29(8): 2293-2296. (in Chinese)

    [9] Wang L, Lou L M, Yang C L, et al. Portrait drawing from corresponding range and intensity images[J]. Frontiers of Information Technology & Electronic Engineering, 2013, 14(7): 530-541.

    [10] Zhang Xiuli, Li Qi, Wang Qi. Simulation of infrared-active-passive imaging fusion based on pixel-level[J]. Infrared and Laser Engineering, 2008, 37(S3): 104-107. (in Chinese)

    [11] Yu Zhiwen, Daxing Wang, Jane You, et al. Progressive subspace ensemble learning[J]. Pattern Recognition, 2016, 60(C): 692-705.

    [12] Choi J Y, Dae H K, Konstantinos N P, et al. Classifier Ensemble generation and Selection with multiple feature representations for classification applications in computer-aided detection and diagnosis on mammography[J]. Expert Systems with Applications, 2016, 46(C): 106-121.

    [13] Zhu Zhihui, Xie Dejun, Li Wanqing, et al. Abnormal eggs detection based on spectroscopy technology and multiple classifier fusion[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(2): 312-318. (in Chinese)

    [15] Suraj Z, Gayar N E, Delimata P. A rough set approach to multiple classifier systems[J]. Fundamenta Informaticae, 2006, 72(1-3): 393-406.

    [16] Hu Qinghua, Daren Yu, Zongxia Xie, et al. Eros: ensemble rough subspaces[J]. Pattern Recognition, 2007, 40(12): 3728-3739.

    [17] Kumar Das Asit, Jaya Sil. An efficient classifier design integrating rough set and set oriented database operations[J]. Applied Soft Computing, 2011, 11(2): 2279-2285.

    [18] Guo Yuwei, Jiao Licheng, Wang Shuang, et al. A novel dynamic rough subspace based selective ensemble[J]. Pattern Recognition, 2015, 48(5): 1638-1652.

    [19] Teng Shuhua, Lu Min, Yang Afeng, et al. A weighted uncertainty measure of rough sets based on general binary relation[J]. Chinese Journal of Conputers, 2014, 37(3): 649-665. (in Chinese)

    [21] Huang Yingqing, Zhao Kai, Jiang Xiaoyu, et al. Study on recognition for armored vehicle based on wavelet moment and SVM[J]. Journal of Academy of Armored Force Engineering, 2012, 26(3): 61-64. (in Chinese)

    [22] Mian Ajmal Saeed. Representations and matching techniques for 3d free-form object and face recognition[D]. Perth: University of Western Australia, 2006.

    [23] Chen Hui, Bir Bhanu. 3d free-form object recognition in range images using local surface patches[J]. Pattern Recognition Letters, 2007, 28(10): 1252-1262.

    [24] Guo Yulan, Lu Min, Tan Zhiguo, et al. Fast target recognition in ladar using projection contour feature[J]. Chinese Journal of Lasers, 2012, 39(2): 194-199. (in Chinese)

    [25] Teng Shuhua, Min Lu, Yang Afeng, et al. Efficient attribute reduction from the viewpoint of discernibility[J]. Information Sciences, 2016, 326(C): 297-314.

    [26] Jia Xiuyi, Shang Lin, Zhou Bing, et al. Generalized attribute reduct in rough set theory[J]. Knowledge-Based Systems, 2016, 91(C): 204-218.

    [27] Niu Tao, Yang Fengbao, Wang Xiaoxia, et al. Establishment of set-valued mapping between difference characteristics and fusion algorithms[J]. Infrared and Laser Engineering, 2015, 44(3): 1073-1079. (in Chinese)

    [28] Qi Shengxiang, Ma Jie, Tao Chao, et al. A robust directional saliency-based method for infrared small-target detection under various complex backgrounds[J]. Geoscience and Remote Sensing Letters, IEEE, 2013, 10(3): 495-499.

    [29] Zhang Wenguang, Lu Min, Guo Yulan, et al. LiDAR data processing system based on multi-core DSP[J]. Laser & Infrared, 2015, 45(11): 1385-1391. (in Chinese)

    [30] Peng Hanchuan, Long Fuhui, Ding C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2005, 27(8): 1226-1238.

    [31] Hu Qinghua, Yu Daren, Xie Zongxia. Numerical attribute reduction based on neighborhood granulation and rough approximation[J]. Journal of Software, 2008, 19(3): 640-649. (in Chinese)

    Tong Xuanyue, Wu Ran, Yang Xinfeng, Teng Shuhua, Zhuang Zhiyun. Fusion target recognition method of infrared and laser[J]. Infrared and Laser Engineering, 2018, 47(5): 526003
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