AUTOMATIC EXTRACTION OF ORCHARDS FROM REMOTE SENSING IMAGE BASED ON CATEGORY ATTENTION MECHANISM

Hongyan Liu∗,∗∗ Shun Ren∗,∗∗ Dong Ren∗,∗∗ and Xuan Liu∗,∗∗

References

  1. [1] C.Y. Chen, W.G. Gong, Y.L. Chen, and W.H. Li, Object detection in remote sensing images based on a scene-contextual feature pyramid network, Remote Sensing, 11(3), 2019, 339.
  2. [2] W. Cui, F. Wang, X. He, D.Y. Zhang, X.X. Xu, M. Yao, Z.W. Wang, and J.J. Huang, Multi-scale semantic segmentation and spatial relationship recognition of remote sensing images based on an attention model, Remote Sensing, 11(9), 2019, 1044.
  3. [3] C. He, P.Z. Fang, Z. Zhang, D.H. Xiong, and M.S. Liao, An end-to-end conditional random fields and skip-connected generative adversarial segmentation network for remote sensing images, Remote Sensing, 11(13), 2019, 1604.
  4. [4] K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv:1409.1556, 2014.
  5. [5] F. Chollet, Xception: Deep learning with depthwise separable convolutions, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, 1800–1807.
  6. [6] K.M. He, X.Y. Zhang, S.Q. Ren, and J. Sun, Deep residual learning for image recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, 770–778.
  7. [7] O. Ronneberger, P. Fischer, and T. Brox, U-net: Convolutional networks for biomedical image segmentation, International Conference on Medical Image Computing and ComputerAssisted Intervention, Munich, Germany, 2015, 234–241.
  8. [8] L.C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A.L. Yuille, Semantic image segmentation with deep convolutional nets and fully connected CRFs, Computer Science, (4), 2014, 357–361.
  9. [9] L.C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A.L. Yuille, Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs, IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 2018, 834–848.
  10. [10] L.C. Chen, G. Papandreou, F. Schroff, and H. Adam, Rethinking atrous convolution for semantic image segmentation, CoRR, 2017.
  11. [11] L.C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, Encoder-decoder with atrous separable convolution for semantic image segmentation, European Conference on Computer Vision, Munich, Germany, 2018, 833–851.
  12. [12] H.S. Zhao, J.P. Shi, X.J. Qi, X.G. Wang, and J.Y. Jia, Pyramid scene parsing network, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, 6230–6239.
  13. [13] J. Fu, J. Liu, H. Tian, Y. Li, Y.G. Bao, Z.W. Fang, and H.Q. Lu, Dual attention network for scene segmentation, 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, 2019, 3141–3149.
  14. [14] Z.L. Huang, X.G. Wang, L.C. Huang, C. Huang, Y.C. Wei, and W.Y. Liu, CCNet: Criss-cross attention for semantic segmentation, 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea, 2019, 603–612.
  15. [15] E. Shelhamer, J. Long, and T. Darrell, Fully convolutional networks for semantic segmentation, IEEE Transactions on Pattern Analysis & Machine Intelligence, 39(4), 2017, 640–651.
  16. [16] Z. Wu, Y. Gao, L. Li, J. Xue, and Y. Li, Semantic segmentation of high-resolution remote sensing images using fully convolutional network with adaptive threshold, Connection Science, 31(2), 2019, 169–184.
  17. [17] D.F. Peng, Y.J. Zhang, and H.Y. Guan, End-to-end change detection for high resolution satellite images using improved UNet++, Remote Sensing, 11(11), 2019, 1382.
  18. [18] L.G. Weng, Y.M. Xu, M. Xia, Y.H. Zhang, J. Liu, and Y.Q. Xu, Water areas segmentation from remote sensing images using a separable residual segnet network, International Journal of Geo-Information, 9(4), 2020, 256.
  19. [19] Y.N. Lin, D.Y. Xu, N. Wang, Z. Shi, and Q.X. Chen, Road extraction from very-high-resolution remote sensing images via a nested SE-Deeplab Model, Remote. Sensing, 12(18), 2020, 2985.
  20. [20] X.L. Wang, R. Girshick, A. Gupta, and K.M. He, Nonlocal neural networks, Computer Vision and Pattern Recognition(CVPR), Salt Lake City, UT, USA, 2018, 7794–7803.
  21. [21] G. Huang, Z. Liu, V.D.M. Laurens, and K.Q. Weinberger, Densely connected convolutional networks, Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, 2261–2269.
  22. [22] F. Zhang, Y.Q. Chen, Z.H. Li, Z.B. Hong, J.T. Liu, F.F. Ma, J.Y. Han, and E. Ding, ACFNet: Attentional class feature network for semantic segmentation, 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea, 2019, 6797–6806.
  23. [23] L. Zhang, D. Ren, Z.Y. Huang, S.H. Lei, and C. Zhang, Image stitching method based on projective interpolation, International Journal of Robotics and Automation, 31(5), 2016, 439–445.

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