Special Issue: Automatic Detection and Assessment of Bridge Structures

CULTIVATED LAND SEGMENTATION OF REMOTE SENSING IMAGE BASED ON PSPNET OF ATTENTION MECHANISM

References

  1. [1] Z. Dong, M. Wang, and D.R. Li, A high resolution remote sensing image segmentation method by combining superpixels with minimum spanning tree. Acta Geodaetica et Cartographica sinica, 46(6), 2017, 734–743.
  2. [2] D.W. Liu, L. Han, and X.Y. Han, High spatial resolution remote sensing image classification based on deep learning, Acta Optica Sinica, 36(4), 2016, 1–9.
  3. [3] C. Liu, L. Hong, J, Chen, S. Chu, and M. Deng, Fusion of pixel-based and multi-scale region-based features for the classification of high-resolution remote sensing image. Journal of Remote Sensing, 19(2), 2015, 228–239.
  4. [4] S.L. Lewisgonzales, N. Nagle, and K. Grace, Accuracy of Supervised Classification of Cropland in Sub-Saharan Africa (Knoxville: University of Tennessee, 2015), 3386–3392.
  5. [5] X. Blaes, L. Vanhalle, and P. Defourny, Efficiency of crop identification based on optical and SAR image time series, Remote Sensing of Environment, 96, 2005, 352–365.
  6. [6] Y. Mu, M.Q. Wu, and Z. Niu, Method of remote sensing extraction of cultivated land area under complex conditions in southern region, Remote Sensing Technology and Application, 35(5), 2020, 1127–1135 (in Chinese).
  7. [7] D. Liu, L. Han, and X.Y. Han, High spatial resolution remote sensing image classification based on deep learning, Acta Optica Sinica, 36(4), 2016, 4288–4289.
  8. [8] C. Liu, L. Hong, J. Chen, and M. Deng, Fusion of pixel-based and multi-scale region-based features for the classification of high-resolution remote sensing image, Journal of Remote Sensing, 19(2), 2015, 228–239.
  9. [9] J. Jin, Z.R. Zou, and C. Tao, Compressed text on based high resolution remote sensing image classification, Acta Geodaetica et Cartographica Sinica, 43(5), 2014, 493—499.
  10. [10] Z.C. Wu, Z.W. Hu, Q. Zhang, and W. Cui, On combining spectral, textural and shape features for remote sensing image segmentation, Acta Geodaetica et Cartographica sinica, 42(01), 2013, 44–50.
  11. [11] L. Zhang, D. Ren, Z.Y. Huang, and S.M. Lei, Image stitching method based on projective interpolation, International Journal of Robotics and Automation, 31(5), 2016, 439–445.
  12. [12] S. Paisitkriangkrai, J. Sherrah, and P. Janney, Effffective semantic pixel labelling with convolutional networks and conditional random fifields, IEEE, Computer Vision & Pattern Recognition Workshops, Boston, USA, 2015, 36–43.
  13. [13] G. Papandreou, I. Kokkinos, and P.A. Savalle, Untangling local and global deformations in deep convolutional networks for image classifification and sliding windowdetection, Computer Vision & Pattern Recognition Workshops,Boston, USA, 2015, 1406–1419.
  14. [14] B. Vijay, K. Alex, and C. Roberto, SegNet: A deep convolutional encoder-decoder architecture for image segmentation, Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2017, 1109–1112.
  15. [15] F. Zhang, B. Du, and L. Zhang, Scene classification via a gradient boosting random convolutional network framework, Geoscience and Remote Sensing, 54, 2016, 1793–1803.
  16. [16] O. Ronneberger, P. Fischer, and T. Brox, U-net: Convolutional networks for biomedical image segmentation, International Conference on Medical Image Computing and Computer Assisted Intervention, Springer International Publishing, Munich, Germany, 2015, 234–241.
  17. [17] 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, Transactions on Pattern Analysis and Machine Intelligence, 40(4), 2018, 834–848.
  18. [18] J. Long, F. Shelhamer, and T. Darrell, Fully convolutional networks for semantic segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, Boston, USA, 39(4), 2015, 640–651.
  19. [19] S. Selim, I. Vladimir, B. Alexander, and S. Alexey, Feature pyramid network for multi-class land segmentation, Conference on Computer Vision and Pattern Recognition Workshops, 2018, 272–275.
  20. [20] C. Tian, C. Li, and J. Shi, Dense fusion classmate network for land cover classification, Computer Vision and Pattern Recognition, 2019, 192–196.

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