• Acta Photonica Sinica
  • Vol. 51, Issue 2, 0210007 (2022)
Tianwei YU1, Enrang ZHENG1、*, Junge SHEN2, and Kai WANG3
Author Affiliations
  • 1School of Electrical and Control Engineering,Shaanxi University of Science and Technology,Xi′an710021,China
  • 2Unmanned System Research Institute,Northwestern Polytechnical University,Xi′an710072,China
  • 3Henan Key Laboratory of Underwater Intelligent Equipment,Zhengzhou 450000,China
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    DOI: 10.3788/gzxb20225102.0210007 Cite this Article
    Tianwei YU, Enrang ZHENG, Junge SHEN, Kai WANG. Optical Remote Sensing Image Scene Classification Based on Multi-level Cross-layer Bilinear Fusion[J]. Acta Photonica Sinica, 2022, 51(2): 0210007 Copy Citation Text show less
    References

    [1] Suhui XU, Xiaodong MU, Peng ZHAO et al. Scene classification of remote sensing image based on multi-scale feature and deep neural network. Acta Geodaetica et Cartographica Sinica, 45, 834-840(2016).

    [2] Jianfeng REN, Xudong JIANG, Junsong YUAN. Learning LBP structure by maximizing the conditional mutual information. Pattern Recognition, 48, 3180-3190(2015).

    [3] Bin LUO, Shujing JIANG, Liangpei ZHANG. Indexing of remote sensing images with different resolutions by multiple features. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6, 1899-1912(2013).

    [4] Gong CHENG, Peicheng ZHOU, Junwei HAN et al. Auto-encoder-based shared mid-level visual dictionary learning for scene classification using very high resolution remote sensing images. IET Computer Vision, 9, 639-647(2015).

    [5] Yi YANG, S NEWSAM. Bag-of-visual-words and spatial extensions for land-use classification, 270-279(2010).

    [6] Fan HU, Guisong XIA, Jingwen HU et al. Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sensing, 7, 14680-14707(2015).

    [7] K NOGUEIRA, O A B PENATTI, J A SANTOS. Towards better exploiting convolutional neural networks for remote sensing scene classification. Pattern Recognition, 61, 539-556(2017).

    [8] Chenhui MA, Xiaodong MU, Dexuan SHA. Multi-layers feature fusion of convolutional neural network for scene classification of remote sensing. IEEE Access, 99, 1-1(2019).

    [9] Yuan YUAN, Jie FANG, Xiaoqiang LU et al. Remote sensing image scene classification using rearranged local features. IEEE Transactions on Geoscience and Remote Sensing, 57, 1779-1792(2019).

    [10] K SIMONYAN, A ZISSERMAN. Very deep convolutional networks for large-scale image recognition. arXiv preprint, arXiv(2014).

    [11] Bin ZHANG, Yongjun ZHANG, Shugen WANG. A lightweight and discriminative model for remote sensing scene classification with multidilation pooling module. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 2636-2653(2019).

    [12] Panqu WANG, Pengfei CHEN, Ye YUAN et al. Understanding convolution for semantic segmentation, 1451-1460(2018).

    [13] T Y LIN, A ROYCHOWDHURY, S MAJI. Bilinear cnn models for fine-grained visual recognition. Proceedings of the IEEE International Conference on Computer Vision, 1449-1457(2015).

    [14] Guisong XIA, Jingwen HU, Fan HU et al. AID: A benchmark dataset for performance evaluation of aerial scene classification. IEEE Transactions on Geoscience and Remote Sensing, 55, 3965-3981(2017).

    [15] Weixun ZHOU, S NEWSAM, Congmin LI et al. PatternNet: a benchmark dataset for performance evaluation of remote sensing image retrieval. ISPRS Journal Photogram Remote Sensing, 145, 97-209(2018).

    [16] Hao SUN, Siyuan LI, Xiangtao ZHENG et al. Remote sensing scene classification by gated bidirectional network. IEEE Transactions on Geoscience and Remote Sensing, 58, 82-96(2020).

    [17] Qi WANG, Shaoteng LIU, J CHANUSSOT et al. Scene classification with recurrent attention of VHR remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 57, 1155-1167(2019).

    [18] S CHAIB, Huang LIU, Yanfeng GU et al. Deep feature fusion for VHR remote sensing scene classification. IEEE Transactions on Geoscience and Remote Sensing, 55, 4775-4784(2017).

    [19] Xuning LIU, Yong ZHOU, Jiaqi ZHAO et al. Siamese convolutional neural networks for remote sensing scene classification. IEEE Geoscience and Remote Sensing Letters, 16, 1200-1204(2019).

    [20] Yulong YU, Fuxian LIU. A two-stream deep fusion frame work for high-resolution aerial scene classification. Computational Intelligence and Neuroscience, 2018, 1-13(2018).

    [21] Xiaoliang QIAN, Jia LI, Gong CHENG et al. Evaluation of the effect of feature extraction strategy on the performance of high-resolution sensing image classification. Journal of Remote Sensing, 22, 758-776(2018).

    [22] Yiyou GUO, Jinsheng JI, Xiankai LU et al. Global-local attention network for aerial scene classification. IEEE Access, 7, 67200-67212(2019).

    [23] Dongen GUO, Yang XIA, Xiaobo LUO. Scene classification of remote sensing images based on saliency dual attention residual network. IEEE Access, 8, 1-1(2020).

    [24] M A SHAFAEY, A M SALEM, H M EBEID et al. Comparison of CNNs for remote sensing scene classification, 27-32(2018).

    Tianwei YU, Enrang ZHENG, Junge SHEN, Kai WANG. Optical Remote Sensing Image Scene Classification Based on Multi-level Cross-layer Bilinear Fusion[J]. Acta Photonica Sinica, 2022, 51(2): 0210007
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