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Super-resolution flood inundation mapping for multispectral image based on super-pixel scale

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

    Hubei Key Laboratory of Regional Development and Environment Response, Hubei University, Wuhan 430062, China

    State Key Laboratory of Geo-Information Engineering, Xi’an Research Institute of Surveying and Mapping, Xi’an 710054, China

  • Email：Pengwang_B614080003@nuaa.edu.cn
  • Introduction：王鹏，1989年生，男，副教授，研究方向为遥感数据处理和模式识别。E-mail: Pengwang_B614080003@nuaa.edu.cn
WANG Peng123,  
  • Affiliation：

    Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

YAO Hongyu1,  
  • Affiliation：

    Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

ZHANG Gong1

Resümee

Super Resolution Mapping (SRM) technology can effectively handle mixed pixels in remote sensing image and obtain the accurate distribution information of land-cover class. SRM technology is currently successfully applied to flood inundation mapping for multispectral image, which is called Super Resolution Flood Inundation Mapping (SRFIM). However, the existing SRFIM methods are often based on pixel-scale spatial correlation. This method considers the spatial relationship between pixels in the set rectangular window, but the shape of the inundation area or the non-inundation area is irregular in reality. Thus, the pixel-scale spatial correlation is insufficiently accurate, which affects the final accuracy of flood inundation mapping. Super-resolution flood inundation mapping of multispectral image based on super-pixel scale spatial correlation (SSSC-SRFIM) is proposed to solve the abovementioned problem.In SSSC-SRFIM, the original coarse multispectral image is first improved by bicubic interpolation to obtain the improved image, and the fractional image with the proportion value of each subpixel belonging to inundation area is obtained by unmixing the improved image. The first principal component of the improved image is then extracted by principal component analysis, and the image segmentation based on multi-resolution is used to segment the first principal component to obtain the super-pixels with irregular shape. Next, the fractional image and super-pixels are integrated, and the random walk algorithm is introduced to calculate the super-pixel-scale spatial correlation. Finally, according to the super-pixel-scale spatial correlation, the label of the inundation area or the non-inundation area is assigned to each sub-pixel by the class allocation method based on the unit of object. Thus, the final result of flood inundation mapping is produced.Two Landsat 8 OLI multispectral images are used to evaluate the method. The proposed SSSC-SRFIM method has better performance than the traditional methods.In the proposed SSSC-SRFIM, the super-pixel-scale spatial correlation is more accurate than pixel-scale spatial correlation because the irregular distribution shape of the actual inundation and non-inundation areas is considered. Therefore, better flood inundation mapping result can be obtained by the proposed SSSC-SRFIM.

Schlüsselwort

remote sensing;multispectral image;flood inundation;super-resolution mapping;super-pixel;image segmentation;random walk algorithm

References

  1. 1.
    Atkinson P M. 2005. Sub-pixel target mapping from soft-classified, remotely sensed imagery. Photogrammetric Engineering and Remote Sensing, 71(7): 839-846
  2. 2.
    Chen Y H, Ge Y, Heuvelink G B M, An R and Chen Y. 2018. Object-based superresolution land-cover mapping from remotely sensed imagery. IEEE Transactions on Geoscience and Remote Sensing, 56(1): 328-340
  3. 3.
    Chen Y H, Ge Y, Heuvelink G B M, Hu J L and Jiang Y. 2015a. Hybrid constraints of pure and mixed pixels for soft-then-hard super-resolution mapping with multiple shifted images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(5): 2040-2052
  4. 4.
    Chen Y H, Ge Y and Song D J. 2015b. Superresolution land-cover mapping based on high-accuracy surface modeling. IEEE Geoscience and Remote Sensing Letters, 12(12): 2516-2520
  5. 5.
    Drăgut L, Tiede D and Levick S R. 2010. ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. International Journal of Geographical Information Science, 24(6): 859-871
  6. 6.
    Ge Y, Chen Y H, Stein A, Li S P and Hu J L. 2016. Enhanced subpixel mapping with spatial distribution patterns of geographical objects. IEEE Transactions on Geoscience and Remote Sensing, 54(4): 2356-2370
  7. 7.
    Ji T K and Zhao Z M. 2005. The theory to evaluate the image spatial resolution quantitatively. Journal of Remote Sensing, 9(4): 486-493
  8. 8.
    Jin H R, Mountrakis G and Li P J. 2012. A super-resolution mapping method using local indicator variograms. International Journal of Remote Sensing, 33(24): 7747-7773
  9. 9.
    Kang X D, Li S T, Fang L Y, Li M X and Benediktsson J A. 2015. Extended random walker-based classification of hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 53(1): 144-153
  10. 10.
    Li L Y, Chen Y, Xu T B, Liu R, Shi K F and Huang C. 2015a. Super-resolution mapping of wetland inundation from remote sensing imagery based on integration of back-propagation neural network and genetic algorithm. Remote Sensing of Environment, 164: 142-154
  11. 11.
    Li L Y, Chen Y, Xu T B, Meng L K, Huang C and Shi K F. 2020. Spatial attraction models coupled with Elman neural networks for enhancing sub-pixel urban inundation mapping. Remote Sensing, 12(13): 2068
  12. 12.
    Li L Y, Chen Y, Xu T B, Shi K F, Huang C, Liu R, Lu B B and Meng L K. 2019. Enhanced super-resolution mapping of urban floods based on the fusion of support vector machine and general regression neural network. IEEE Geoscience and Remote Sensing Letters, 16(8): 1269-1273
  13. 13.
    Li L Y, Chen Y, Yu X, Liu R and Huang C. 2015b. Sub-pixel flood inundation mapping from multispectral remotely sensed images based on discrete particle swarm optimization. ISPRS Journal of Photogrammetry and Remote Sensing, 101: 10-21
  14. 14.
    Li L Y, Xu T B and Chen Y. 2016. Improved urban flooding mapping from remote sensing images using generalized regression neural network-based super-resolution algorithm. Remote Sensing, 8(8): 625
  15. 15.
    Li X D, Du Y, Ling F, Feng Q and Fu B T. 2014. Superresolution mapping of remotely sensed image based on Hopfield neural network with anisotropic spatial dependence model. IEEE Geoscience and Remote Sensing Letters, 11(7): 1265-1269
  16. 16.
    Ling F, Du Y, Zhang Y H, Li X D and Xiao F. 2015. Burned-area mapping at the subpixel scale with MODIS images. IEEE Geoscience and Remote Sensing Letters, 12(9): 1963-1967
  17. 17.
    Ling F and Foody G M. 2019. Super-resolution land cover mapping by deep learning. Remote Sensing Letters, 10(6): 598-606
  18. 18.
    Ling F, Li W B, Du Y and Li X D. 2011. Land cover change mapping at the subpixel scale with different spatial-resolution remotely sensed imagery. IEEE Geoscience and Remote Sensing Letters, 8(1): 182-186
  19. 19.
    Ling F, Li X D, Xiao F, Fang S M and Du Y. 2012. Object-based sub-pixel mapping of buildings incorporating the prior shape information from remotely sensed imagery. International Journal of Applied Earth Observation and Geoinformation, 18: 283-292
  20. 20.
    Ling F, Wu S J, Xiao F, Wu K and Li X D. 2011. Sub-pixel mapping of remotely sensed imagery: a review. Journal of Image and Graphics, 16(8): 1335-1345
  21. 21.
    Ma A L, Zhong Y F, He D and Zhang L P. 2018. Multiobjective subpixel land-cover mapping. IEEE Transactions on Geoscience and Remote Sensing, 56(1): 422-435
  22. 22.
    Makido Y, Shortridge A and Messina J P. 2007. Assessing alternatives for modeling the spatial distribution of multiple land-cover classes at sub-pixel scales. Photogrammetric Engineering and Remote Sensing, 73(8): 935-943
  23. 23.
    Olthof I and Tolszczuk-Leclerc S. 2018. Comparing Landsat and RADARSAT for current and historical dynamic flood Mapping. Remote Sensing, 10(5): 780
  24. 24.
    Verhoeye J and De Wulf R. 2002. Land cover mapping at sub-pixel scales using linear optimization techniques. Remote Sensing of Environment, 79(1): 96-104
  25. 25.
    Villa A, Chanussot J, Benediktsson J A and Jutten C. 2011. Spectral unmixing for the classification of hyperspectral images at a finer spatial resolution. IEEE Journal of Selected Topics in Signal Processing, 5(3): 521-533
  26. 26.
    Wang L G, Liu D F, Wang Q M and Wang Y. 2013. Spectral unmixing model based on least squares support vector machine with unmixing residue constraints. IEEE Geoscience and Remote Sensing Letters, 10(6): 1592-1596
  27. 27.
    Wang P, Wang L G and Chanussot J. 2016. Soft-then-hard subpixel land cover mapping based on spatial-spectral interpolation. IEEE Geoscience and Remote Sensing Letters, 13(12): 1851-1854
  28. 28.
    Wang P, Wang L G, Leung H and Zhang G. 2019. Subpixel mapping based on hopfield neural network with more prior information. IEEE Geoscience and Remote Sensing Letters, 16(8): 1284-1288
  29. 29.
    Wang P, Wang L G, Mura M D and Chanussot J. 2017. Using multiple subpixel shifted images with spatial-spectral information in soft-then-hard subpixel mapping. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(6): 2950-2959
  30. 30.
    Wang Q M, Atkinson P M and Shi W Z. 2015. Fast subpixel mapping algorithms for subpixel resolution change detection. IEEE Transactions on Geoscience and Remote Sensing, 53(4): 1692-1706
  31. 31.
    Wang Q M, Shi W Z and Atkinson P M. 2014b. Sub-pixel mapping of remote sensing images based on radial basis function interpolation. SPRS Journal of Photogrammetry and Remote Sensing, 92: 1-15
  32. 32.
    Wang Q M, Shi W Z and Wang L G. 2014a. Allocating classes for soft-then-hard subpixel mapping algorithms in units of class. IEEE Transactions on Geoscience and Remote Sensing, 52(5): 2940-2959
  33. 33.
    Wang Q M, Wang L G and Liu D F. 2012. Particle swarm optimization-based sub-pixel mapping for remote-sensing imagery. International Journal of Remote Sensing, 33(20): 6480-6496
  34. 34.
    Xie H, Luo X, Xu X, Pan H Y and Tong X H. 2016. Automated subpixel surface water mapping from heterogeneous urban environments using Landsat 8 OLI Imagery. Remote Sensing, 8(7): 584
  35. 35.
    Xu X, Zhong Y F, Zhang L P and Li P X. 2011. A sub-pixel mapping algorithm based on BP neural network with spatial autocorrelation function for remote sensing imagery. Acta Geodaetica et Cartographica Sinica, 40(3): 307-311
  36. 36.
    Xu X, Zhong Y F, Zhang L P and Zhang H Y. 2013. Sub-pixel mapping based on a MAP model with multiple shifted hyperspectral imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(2): 580-593
  37. 37.
    Zhang Y H, Ling F, Li X D and Du Y. 2015. Super-resolution land cover mapping using multiscale self-similarity redundancy. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(11): 5130-5145

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