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Optimal Minnaert topographic correction model based on land cover classification

  • role： First author第一作者
  • Affiliation：

    School of Computer and Information Engineering, Henan University, Kaifeng 475000, China

    Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475000, China

  • Email：linyh@henu.edu.cn
  • Introduction：林英豪，研究方向为植被定量遥感应用。E-mail： linyh@henu.edu.cn
LIN Yinghao12,  
  • Affiliation：

    School of Computer and Information Engineering, Henan University, Kaifeng 475000, China

    Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475000, China

JIN Yan12,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Computer and Information Engineering, Henan University, Kaifeng 475000, China

    Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475000, China

  • Email：shenxj@henu.edu.cn
  • Introduction：沈夏炯，研究方向为空间数据分析、分布/并行计算、形式概念分析。E-mail： shenxj@henu.edu.cn
SHEN Xiajiong12*,  
  • Affiliation：

    School of Computer and Information Engineering, Henan University, Kaifeng 475000, China

    Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475000, China

ZHOU Liming12

resumen

Topographic correction can reduce the problem of uneven solar radiation reception and surface reflectance distortion caused by terrain undulations in complex terrain areas, thus improving the quality of remote sensing images and the accuracy of remote sensing information extraction. However, existing topographic correction models have some problems, such as overcorrection, unstable effect of each band correction, and unsatisfactory correction accuracy.This work proposes a corrected Minnaert topographic correction model, named the CMinnaert topographic correction model, which considers the type of land cover, based on the correlation between the k coefficient of the Minnaert topographic correction model and the bidirectional reflection characteristics of the ground object. Two methods are used in the pre classification of surface features: the first level classification of land cover types and the classification of vegetation density to verify the stability of the CMinnaert model. The best classification scheme of land cover types is proposed. First, a corrected image was pre-classified into land cover types, and the k coefficient was fitted to determine the land cover types in different places. Finally, Minnaert topographic correction was applied to the remote sensing data by using the k coefficient of the land cover type in each area. A Landsat 8/OLI image of Shangcheng County, Henan Province, China was used as experimental data.The cosine correction model, the Sun Canopy Sensor (SCS) correction model, the Minnaert correction model, the Minnaert correction model based on slope, and the CMinnaert correction model were used to perform topographic correction of images in the research area. Visual comparison and statistical data analysis were used to evaluate the topographic correction performance of each algorithm. Results show that the CMinnaert correction model can effectively weaken the influence of the terrain effect on the radiance value of the remote sensing image: the CMinnaert correction model for the first level classification of land cover types can effectively reduce the linear fitting R2 of radiance and cosine of solar incidence angle at each band compared with the original image and the other four topographic correction results, and no over correction phenomenon occurred. Furthermore, the CMinnaert model of the vegetation density classification can effectively weaken the overcorrection problem of other correction models in bands 1 and 5. The linear fitting R2 of the radiance and cosine of the solar incidence angle in the other bands is the lowest of the six models. The CMinnaert model of two pre classification methods is more stable and better than the other four topographic correction models. The results of the visual comparison, cosine correlation analysis of the solar incidence angle, radiance histogram, and spectral characteristic analysis of the sunny and shady slopes are basically consistent.This study recommends that the first level classification should be used in CMinnaert topographic correction considering the algorithm efficiency and practical application ability of the CMinnaert model.

palabra clave

remote sensing;topographic correction;Minnaert model;land cover type;Landsat 8/OLI

References

  1. 1.
    Cavayas F. 1987. Modelling and correction of topographic effect using multi-temporal satellite images. Canadian Journal of Remote Sensing, 13(2): 49-67
  2. 2.
    Chinese Ministry of Land and Resources. 2017. Current land use classification. GB/T 21010-2017
  3. 3.
    Civco D L. 1989. Topographic normalization of landsat thematic mapper digital imagery. Photogrammetric Engineering and Remote Sensing, 55(9): 1303-1309
  4. 4.
    Conese C, Gilabert M A, Maselli F and Bottai L. 1993. Topographic normalization of TM scenes through the use of an atmospheric correction method and digital terrain models. Photogrammetric Engineering and Remote Sensing, 59(12): 1745-1753
  5. 5.
    De Oliveira L M, Galvão L S and Ponzoni F J. 2021. Topographic effects on the determination of hyperspectral vegetation indices: a case study in southeastern Brazil. Geocarto International, 36(19): 2186-2203
  6. 6.
    Dong C, Zhao G X, Meng Y, Li B H and Peng B. 2020. The effect of topographic correction on forest tree species classification accuracy. Remote Sensing, 12(5): 787
  7. 7.
    Duan S B, Yan G J, Mu X H, Shen B and Li X W. 2007. DEM based remotely sensed imagery topographic correction method in mountainous areas. Geography and Geo-Information Science, 23(6): 18-22
  8. 8.
    Fan W L, Li J, Liu Q H, Zhang Q, Yin G F, Li A N, Zeng Y L, Xu B D, Xu X J, Zhou G M and Du H Q. 2018. Topographic correction of forest image data based on the canopy reflectance model for sloping terrains in multiple forward mode. Remote Sensing, 10(5): 717
  9. 9.
    Gao M L, Gong H L, Zhao W J, Chen B B, Chen Z and Shi M. 2016. An improved topographic correction model based on minnaert. GIScience and Remote Sensing, 53(2): 247-264
  10. 10.
    Gao M L, Zhao W J, Gong Z N, Gong H L, Chen Z and Tang X M. 2014. Topographic correction of ZY-3 satellite images and its effects on estimation of shrub leaf biomass in mountainous areas. Remote Sensing, 6(4): 2745-2764
  11. 11.
    Gao Y N and Zhang W C. 2008a. Simplification and modification of a physical topographic correction algorithm for remotely sensed data. Acta Geodaetica et Cartographica Sinica, 37(1): 89-94, 120
  12. 12.
    Gao Y N and Zhang W C. 2008b. Comparison test and research progress of topographic correction on remotely sensed data. Geographical Research, 27(2): 467-477
  13. 13.
    Gatebe C K and King M D. 2016. Airborne spectral BRDF of various surface types (ocean, vegetation, snow, desert, wetlands, cloud decks, smoke layers) for remote sensing applications. Remote Sensing of Environment, 179: 131-148
  14. 14.
    Ge H L, Lu D S, He S Z, Xu A J, Zhou G M and Du H Q. 2008. Pixel-based minnaert correction method for reducing topographic effects on a landsat 7 ETM+ image. Photogrammetric Engineering and Remote Sensing, 74(11): 1343-1350
  15. 15.
    Goslee S C. 2012. Topographic corrections of satellite data for regional monitoring. Photogrammetric Engineering and Remote Sensing, 78(9): 973-981
  16. 16.
    Gu D G and Gillespie A. 1998. Topographic normalization of landsat TM images of forest based on subpixel sun-canopy-sensor geometry. Remote Sensing of Environment, 64(2): 166-175
  17. 17.
    Gupta S K and Shukla D P. 2020. Evaluation of topographic correction methods for LULC preparation based on multi-source DEMs and Landsat-8 imagery. Spatial Information Research, 28(1): 113-127
  18. 18.
    Huang B and Xu L H. 2012. Applied research of topographic correction based on the improved minnaert model. Remote Sensing Technology and Application, 27(2): 183-189
  19. 19.
    Huang W, Zhang L P and Li P X. 2005. An improved topographic correction approach for satellite image. Journal of Image and Graphics, 10(9): 1124-1128
  20. 20.
    Hurni K, Van Den Hoek J and Fox J. 2019. Assessing the spatial, spectral, and temporal consistency of topographically corrected landsat time series composites across the mountainous forests of Nepal. Remote Sensing of Environment, 231: 111225
  21. 21.
    Jiang K, Hu C M, Yu K and Zhao Y C. 2014. Landsat TM/ETM+ topographic correction method based on smoothed terrain and semi-empirical model. Journal of Remote Sensing, 18(2): 287-306
  22. 22.
    Kobayashi S and Sanga-Ngoie K. 2008. The integrated radiometric correction of optical remote sensing imageries. International Journal of Remote Sensing, 29(20): 5957-5985
  23. 23.
    Li Y C. 1994. Topographic effect analysis and correction of digital remote sensing image. Beijing Surveying and Mapping, (2): 14-19
  24. 24.
    Li Y K. 2015. Effects of analytical window and resolution on topographic relief derived using digital elevation models. GIScience and Remote Sensing, 52(4): 462-477
  25. 25.
    Lin Q N, Huang H G, Chen L and Chen E X. 2017. Topographic correction method for steep mountain terrain images. Journal of Remote Sensing, 21(5): 776-784
  26. 26.
    Lin X W, Wen J G, Wu S B, Hao D L, Xiao Q and Liu Q H. 2020. Advances in topographic correction methods for optical remote sensing imageries. Journal of Remote Sensing, 24(8): 958-974
  27. 27.
    Lin Y H. 2019 Angles Normalization Model of Vegetation Canopy Reflectance Based on Geostationary Satellite Remote Sensing Data . Nanjing: Nanjing University
  28. 28.
    Liu S H, Liu Q, Liu Q H, Wen J G and Li X W. 2010. The angular and spectral kernel model for BRDF and albedo retrieval. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3(3): 241-256
  29. 29.
    Liu Z W. 2017. Remote Sensing Inversion of Vegetation Cover Parameters based on Topographic Radiation Correction. Tai’an: Shandong Agricultural University
  30. 30.
    Lü L L, Xie Y W and Dong L L. 2017. The comparison of reflectance based on different terrain correction. Remote Sensing Technology and Application, 32(4): 751-759
  31. 31.
    Melnikova I, Awaya Y, Saitoh T M, Muraoka H and Sasai T. 2018. Estimation of leaf area index in a mountain forest of central Japan with a 30-m spatial resolution based on landsat operational land imager imagery: an application of a simple model for seasonal monitoring. Remote Sensing, 10(2): 179
  32. 32.
    Minnaert M. 1941. The reciprocity principle in lunar photometry. The Astrophysical Journal, 93: 403-410
  33. 33.
    Pi X Y，Zeng Y N and He C Q. 2021. Estimating urban vegetation coverage on the basis of multi-source remote sensing data and temporal mixture analysis. National Remote Sensing Bulletin, 25(6): 1216-1226
  34. 34.
    Reeder D H. 2002. Topographic Correction of Satellite Images: Theory and Application. Hanover. New Hampshire: Dartmouth College
  35. 35.
    Reese H and Olsson H. 2011. C-correction of optical satellite data over alpine vegetation areas: a comparison of sampling strategies for determining the empirical c-parameter. Remote Sensing of Environment, 115(6): 1387-1400
  36. 36.
    Smith J A, Lin T L and Ranson K J. 1980. The lambertian assumption and landsat data. Photogrammetric Engineering and Remote Sensing, 46(9): 1183-1189
  37. 37.
    Soenen S A, Peddle D R and Coburn C A. 2005. SCS+C: a modified sun-canopy-sensor topographic correction in forested terrain. IEEE Transactions on Geoscience and Remote Sensing, 43(9): 2148-2159
  38. 38.
    Szantoi Z and Simonetti D. 2013. Fast and robust topographic correction method for medium resolution satellite imagery using a stratified approach. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(4): 1921-1933
  39. 39.
    Teillet P M, Guindon B and Goodenough D G. 1982. On the slope-aspect correction of multispectral scanner data. Canadian Journal of Remote Sensing, 8(2): 84-106
  40. 40.
    Tokola T, Sarkeala J and Van der Linden M. 2001. Use of topographic correction in landsat TM-based forest interpretation in Nepal. International Journal of Remote Sensing, 22(4): 551-563
  41. 41.
    Wen J G, Liu Q H, Liu Q, Xiao Q and Li X W. 2009. Parametrized BRDF for atmospheric and topographic correction and albedo estimation in Jiangxi rugged terrain, China. International Journal of Remote Sensing, 30(11): 2875-2896
  42. 42.
    Yan G J, Jiang H L, Yan K, Cheng S Y, Song W J, Tong Y Y, Liu Y N, Qi J B, Mu X H, Zhang W M, Xie D H and Zhou H M. 2021. Review of optical multi-angle quantitative remote sensing. National Remote Sensing Bulletin, 25(1): 83-108
  43. 43.
    Yang C J, Huang H, Ni J and Yang D F. 2019. Effects of topographic normalization on the relationship between tropical forest biomass and landsat TM images. Journal of the Indian Society of Remote Sensing, 47(4): 595-601
  44. 44.
    Zhang R L. 2012. The Research on Topographic Correction of Remote Sensing Images based on ETM+ Data. Xi’an: Chang’an University
  45. 45.
    Zhang Y, Wang L, Ai Y F and Bai X J. 2019. An analysis research of terrain correction methods considering the slope ranges based on OLI image. IOP Conference Series: Materials Science and Engineering, 592: 012163

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