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Volume 43 Issue 4
Apr.  2021
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LIU Feng, LI Jiajun, LI Yuhai, GAO Peipei. A Method of Image Classification for Objects with Camouflaged Color Features[J]. Infrared Technology , 2021, 43(4): 334-341.
Citation: LIU Feng, LI Jiajun, LI Yuhai, GAO Peipei. A Method of Image Classification for Objects with Camouflaged Color Features[J]. Infrared Technology , 2021, 43(4): 334-341.
shu

A Method of Image Classification for Objects with Camouflaged Color Features

  • 1.

    State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China

  • 2.

    The 53rd Research Institute, China Electronic Technology Group Corporation, Tianjin 300300, China

  • 3.

    College of Computer Science, Nankai University, Tianjin 300071, China

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  • Received Date: July 16, 2020
  • Revised Date: August 20, 2020
    Graphical Abstract
    • Abstract
  • To solve the classification difficulty caused by different objects with similar color features in an image, this paper proposes an image classification method for objects with camouflaged color features. To alleviate the difficulty of distinguishing color-camouflaged objects in RGB images, this method not only combines the spatial domain features of the neighborhood pixels and the spectral domain features of the hyperspectral data, which realizes the spatial-spectrum joint feature construction, but also uses the improved LLE(local linear embedding) algorithm to accomplish spectral dimensional reduction. The proposed method uses an active learning capsule network to train a hyperspectral data classifier and classifies objects in the scene. Active learning can label more representative samples through the improved active learning function and realized capsule network training based on a minor sample dataset, which reduces the cost of sample labeling and model training significantly, thereby improving the classification performance of the model. Experiments show that the algorithm proposed in this paper can effectively classify camouflaged targets and other natural targets based on our self-made hyperspectral dataset. The average accuracy of camouflaged targets was 91%, and the average accuracy of all target types was 89.9%.
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    • References (12)
  • [1]
    SONG W, LI S, FANG L, et al. Hyperspectral image classification with deep feature fusion network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 99: 1-12. http://ieeexplore.ieee.org/document/8283837/citations
    [2]
    罗建华, 李明奇, 郑泽忠, 等. 基于深度卷积神经网络的高光谱遥感图像分类[J]. 西华大学学报: 自然科学版, 2017, 36(4): 13. https://www.cnki.com.cn/Article/CJFDTOTAL-SCGX201704003.htm

    LUO Jianhua, LI Mingqi, ZHENG Zezhong, et al. Hyperspectral remote sensing images classification using a deep convolutional neural network model[J]. Journal of Xihua University: Natural Science Edition, 2017, 36(4): 13. https://www.cnki.com.cn/Article/CJFDTOTAL-SCGX201704003.htm
    [3]
    CHEN Y, ZHAO X, JIA X. Spectral–spatial classification of hyperspectral data based on deep belief network[J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2015, 8(6): 2381-2392. http://ieeexplore.ieee.org/document/7018910/
    [4]
    ZHAO W, DU S. Learning multiscale and deep representations for classifying remotely sensed imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 113: 155-165. DOI: 10.1016/j.isprsjprs.2016.01.004
    [5]
    张倩, 董安国, 宋睿. 基于多特征和改进自编码的高光谱图像分类[J]. 激光与光电子学进展, 2020, 57(8): 081010. https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ202008012.htm

    ZHANG Qian, DONG Anguo, SONG Rui. Hyperspectral image classification based on multiple features and an improved autoencoder[J]. Laser & Optoelectronics Progress, 2020, 57(8): 081010. https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ202008012.htm
    [6]
    陶志勇, 李杰, 唐晓亮. 融合小波变换与胶囊网络的纹理图像分类算法[J]. 激光与光电子学进展, 2020, 57(24): 241002. https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ202024006.htm

    TAO Zhiyong, LI Jie, TANG Xiaoliang. Texture image classification algorithm combining wavelet transform and capsule network[J]. Laser & Optoelectronics Progress, 2020, 57(24): 241002. https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ202024006.htm
    [7]
    Sabour S, Frosst N, Hinton G E. Dynamic routing between capsules[C]//Advances in Neural Information Processing Systems, 2017: 3856-3866.
    [8]
    Cohn D A, Ghahramani Z, Jordan M I. Active learning with statistical models[J]. Journal of Artificial Intelligence Research, 1996, 4(1): 705-712. http://arxiv.org/abs/cs/9603104v1
    [9]
    Tuia D. Unbiased query-by-bagging active learning for VHR image classification[C]//Proceedings of SPIE, 2010, 7830: 78300K.
    [10]
    Quadrianto N, Petterson J, Smola A J. Distribution matching for transduction[C]//Advances in Neural Information Processing Systems, 2009: 1500-1508.
    [11]
    纪磊, 张欣, 张丽梅, 等. 基于空谱加权近邻的高光谱图像分类算法[J]. 激光与光电子学进展, 2020, 57(6): 061013. https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ202006017.htm

    JI Lei, ZHANG Xin, ZHANG Limei, et al. Hyperspectral image classification algorithm based on space-spectral weighted nearest neighbor[J]. Laser & Optoelectronics Progress, 2020, 57(6): 061013. https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ202006017.htm
    [12]
    WANG J. Locally Linear Embedding[M]// Geometric Structure of High -Dimensional Data and Dimensionality Reduction, Springer Berlin Heidelberg, 2012: 203-220.
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