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CN 34-1054/N

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Ocean remote sensing image auto-annotation based on DBNMI model

  • College of Information and Technology, Shanghai Ocean University, Shanghai 201306, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2017.07.001
  • Received Date: 28 August 2016
  • Rev Recd Date: 08 December 2016
  • Publish Date: 31 July 2017
    Abstract

  • Abstract

    Bridge the semantic gap between low-level visual feature and high-level semantic concepts has been the subject of intensive investigation on large scale remote sensing image management for years in order to improve the accuracy of automatic image annotation. An ocean remote sensing image auto-annotation method based on DBNMI model was proposed for contributions of semantic similarity about different regions of ocean remote sensing images. Initial remote sensing images were adaptively segmented, ocean remote sensing images were divided into background and the object region by means of a coarse-grained method, the relationship between low-level visual feature and high-level semantics label of the object region was modeled automatically, using DBN model, and the co-occurrence relations and adversarial relations between semantic concepts for improving image annotation results were calculated. The proposed approach is evaluated on a public remote sensing image dataset. The experimental results show a satisfactory improvement on accuracy.

    Abstract

    Bridge the semantic gap between low-level visual feature and high-level semantic concepts has been the subject of intensive investigation on large scale remote sensing image management for years in order to improve the accuracy of automatic image annotation. An ocean remote sensing image auto-annotation method based on DBNMI model was proposed for contributions of semantic similarity about different regions of ocean remote sensing images. Initial remote sensing images were adaptively segmented, ocean remote sensing images were divided into background and the object region by means of a coarse-grained method, the relationship between low-level visual feature and high-level semantics label of the object region was modeled automatically, using DBN model, and the co-occurrence relations and adversarial relations between semantic concepts for improving image annotation results were calculated. The proposed approach is evaluated on a public remote sensing image dataset. The experimental results show a satisfactory improvement on accuracy.
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    • References(15)
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    [1]
    BRATASANU D, NEDELCU I, DATCU M. Bridging the semantic gap for satellite image annotation and automatic mapping applications[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2011, 4(1): 193-204.
    [2]
    郑歆慰,胡岩峰,孙显,等. 基于空间约束多特征联合稀疏编码的遥感图像标注方法研究[J]. 电子与信息学报, 2014, 36(8): 1891-1898.
    [3]
    LINOU M, MATRE H, DATCU M. Semantic annotation of satellite images using latent dirichlet allocation[J]. Geoscience and Remote Sensing Letters, 2010, 7(1): 28-32.
    [4]
    LUO W, LI H L, LIU G H. Automatic annotation of multispectral satellite images using author-topic model[J]. Geoscience and Remote Sensing Letters, 2012, 9(4): 634-638.
    [5]
    LUO W, LI H L, LIU G H, et al. Semantic annotation of satellite images using author-genre-topic model[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(2): 1356-1368.
    [6]
    CHEN K M, JIAN P, ZHOU Z X, et al. Semantic annotation of high-resolution remote sensing images via Gaussian process multi-instance multilabel learning[J]. Geoscience and Remote Sensing Letters, 2013, 10(6): 1285-1289.
    [7]
    WEI Y C, XIA W, HUANG J S, et al. CNN: Single-label to multi-label[J]. Computer Science, 2014: arXiv:1406.5726.
    [8]
    杨阳, 张文生. 基于深度学习的图像自动标注算法[J]. 数据采集与处理, 2015, 30(1): 88-98.
    [9]
    吕启, 窦勇, 牛新,等. 基于DBN模型的遥感图像分类[J]. 计算机研究与发展, 2014, 51(9): 1911-1918.
    [10]
    高常鑫, 桑农. 基于深度学习的高分辨率遥感影像目标检测[J]. 测绘通报, 2014, (S1):108-111.
    [11]
    黄志坚,黎湘,徐帆江. 基于视觉复杂度的自适应尺度遥感影像分割[J]. 电子与信息学报, 2013, 35(8): 1786-1792.
    [12]
    葛永,吴秀清,洪日昌. 基于多示例学习的遥感图像检索[J]. 中国科学技术大学学报, 2009, 39(2): 132-136.
    GE Y, WU X Q, HONG R C. Remote sensing image retrieval based on multiple instance learning[J]. Journal of University of Science and Technology of China, 2009, 39(2): 132-136.
    [13]
    HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507.
    [14]
    YANG Y, NEWSAM S. Bag-of-visual-words and spatial extensions for land-use classification[C]// Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems. San Jose, USA: ACM Press, 2010: 270-279.
    [15]
    CHANG C C, LIN C J. LIBSVM: A library for support vector machines[J]. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3): 1-27.
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