Anterior|Próximo

Reconhecimento de culturas baseado em características multângulo do satélite GF-7

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China

    School of Land Science and Technology, China University of Geosciences, Beijing 100083, China

  • Email：1045376795@qq.com
  • Introduction：孙智虎，研究方向为农业遥感。E-mail： 1045376795@qq.com
SUN Zhihu12,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China

    Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

  • Email：zhangjs@bnu.edu.cn
  • Introduction：张锦水，研究方向为深度学习与遥感大数据挖掘。E-mail： zhangjs@bnu.edu.cn
ZHANG Jinshui134*,  
  • Affiliation：

    School of Land Science and Technology, China University of Geosciences, Beijing 100083, China

HONG Youtang2,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China

    Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

YANG Junwen134,  
  • Affiliation：

    Beijing Polytechnic College, Beijing 100042, China

ZHU Shuang5

Resumo

A observação terrestre multângulo por sensoriamento remoto pode fornecer características mais ricas e multidirecionais, melhorando a distinguibilidade entre os tipos de cobertura do solo, estabelecendo uma base sólida de dados para o reconhecimento preciso da cobertura do solo. O GF-7 é o primeiro satélite chinês de mapeamento submétrico após o satélite ZY-3, o que oferece uma oportunidade para usar características multângulo para resolver o problema de "objetos diferentes com espectro igual" e melhorar a precisão do reconhecimento de culturas. Este artigo utiliza dados pancromáticos frontais, traseiros e dados multiespectrais traseiros do GF-7, diversas combinações de características foram inseridas em um classificador de máquina de vetor de suporte para classificação. Em comparação com características espectrais e de textura, analisou-se o impacto das características multângulo na precisão do reconhecimento de culturas. Os resultados mostram que, em comparação com o uso apenas de características espectrais, a combinação de características espectrais e diferença angular melhorou a precisão do mapeamento do alho e do trigo de inverno em 4,07% e 3,15%, respectivamente, e a precisão do usuário aumentou 6,73% e 2,12%, respectivamente; em comparação com o uso de características espectrais e de textura, a combinação de características espectrais, de textura e diferença angular melhorou a precisão do mapeamento do alho e do trigo de inverno em 3,14% e 1,01%, respectivamente, e a precisão do usuário aumentou 5,11% e 0,67%, respectivamente. A análise pelo teste de McNemar mostrou que essa melhoria na precisão da classificação é estável, e o uso das características de diferença angular pode melhorar efetivamente a precisão do reconhecimento de culturas. A razão é que as características multângulo possuem respostas espectrais características para diferentes tipos de culturas durante a observação multângulo, aumentando assim a distinguibilidade entre culturas e garantindo a precisão do reconhecimento de culturas por sensoriamento remoto.

Palavras-chave

GF-7;máquina de vetores de suporte;diferença angular;sensoriamento remoto;trigo de inverno;alho;agricultura

References

  1. 1.
    Alshehhi R, Marpu P R, Woon W L and Mura M D. 2017. Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks. ISPRS Journal of Photogrammetry and Remote Sensing, 130: 139-149
  2. 2.
    Chang C C and Lin C J. 2011. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(3): 27
  3. 3.
    Chopping M, Moisen G G, Su L H, Laliberte A, Rango A, Martonchik J V and Peters D P C. 2008. Large area mapping of southwestern forest crown cover, canopy height, and biomass using the NASA Multiangle Imaging Spectro-Radiometer. Remote Sensing of Environment, 112(5): 2051-2063
  4. 4.
    Clayton D G. 1971. Algorithm AS 46: gram-Schmidt Orthogonalization. Applied Statistics, 20(3): 335-338
  5. 5.
    Congalton R G. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1): 35-46
  6. 6.
    Dehghan H and Ghassemian H. 2006. Measurement of uncertainty by the entropy: application to the classification of MSS data. International Journal of Remote Sensing, 27(18): 4005-4014
  7. 7.
    Duro D C, Franklin S E and Dubé M G. 2012. A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sensing of Environment, 118: 259-272
  8. 8.
    Foody G M. 2002. Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80(1): 185-201
  9. 9.
    Foody G M and Atkinson P M. 2002. Uncertainty in Remote Sensing and GIS. Chichester: John Wiley and Sons
  10. 10.
    Haralick R M. 1979. Statistical and structural approaches to texture. Proceedings of the IEEE, 67(5): 786-804
  11. 11.
    Huang X, Chen H J and Gong J Y. 2018. Angular difference feature extraction for urban scene classification using ZY-3 multi-angle high-resolution satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 135: 127-141
  12. 12.
    Huang X, Lu Q K and Zhang L P. 2014. A multi-index learning approach for classification of high-resolution remotely sensed images over urban areas. ISPRS Journal of Photogrammetry and Remote Sensing, 90: 36-48
  13. 13.
    Khatami R, Mountrakis G and Stehman S V. 2016. A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: general guidelines for practitioners and future research. Remote Sensing of Environment, 177: 89-100
  14. 14.
    Lan Z Y and Liu Y. 2018. Study on multi-scale window determination for GLCM texture description in high-resolution remote sensing image geo-analysis supported by GIS and domain knowledge. ISPRS International Journal of Geo-Information, 7(5): 175
  15. 15.
    Li L, She M Y and Luo H. 2014. Comparison on fusion algorithms of ZY-3 panchromatic and multi-spectral images. Transactions of the Chinese Society of Agricultural Engineering, 30(16): 157-165
  16. 16.
    Li X J, Chen W T, Cheng X W and Wang L Z. 2016. A comparison of machine learning algorithms for mapping of complex surface mined and agricultural landscapes using ZiYuan-3 stereo satellite imagery. Remote Sensing, 8(6): 514
  17. 17.
    Liu H F, Yang Y B, Yu S, Kong L T and Zhang Y. 2014. Adaptability evaluation of different fusion methods on ZY-3 and Landsat8 images. Remote Sensing For Land and Resources, 26(4): 63-70
  18. 18.
    Liu X S. 2015. Study on soil bidirectional reflectance characteristics and application of soil standard spectral library. Wuhan: Huazhong Agricultural University
  19. 19.
    Matasci G, Longbotham N, Pacifici F, Kanevski M and Tuia D. 2015. Understanding angular effects in VHR imagery and their significance for urban land-cover model portability: a study of two multi-angle in-track image sequences. ISPRS Journal of Photogrammetry and Remote Sensing, 107: 99-111
  20. 20.
    Roujean J L, Leroy M and Deschamps P Y. 1992. A bidirectional reflectance model of the Earth's surface for the correction of remote sensing data. Journal of Geophysical Research: Atmospheres, 97(D18): 20455-20468
  21. 21.
    Schaepman-Strub G, Schaepman M E, Painter T H, Dangel S and Martonchik J V. 2006. Reflectance quantities in optical remote sensing—definitions and case studies. Remote Sensing of Environment, 103(1): 27-42
  22. 22.
    Song D J, Zhang C M, Yang X X, Li F, Han Y J, Gao S and Dong H Y. 2020. Extracting winter wheat spatial distribution information from GF-2 image. Journal of Remote Sensing (Chinese), 24(5): 596-608
  23. 23.
    Su L H, Chopping M J, Rango A, Martonchik J V and Peters D P C. 2007. Support vector machines for recognition of semi-arid vegetation types using MISR multi-angle imagery. Remote Sensing of Environment, 107(1/2): 299-311
  24. 24.
    Tian J, Reinartz P, d’Angelo P and Ehlers M. 2013. Region-based automatic building and forest change detection on Cartosat-1 stereo imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 79: 226-239
  25. 25.
    Waldner F, Canto G S and Defourny P. 2015. Automated annual cropland mapping using knowledge-based temporal features. ISPRS Journal of Photogrammetry and Remote Sensing, 110: 1-13
  26. 26.
    Wu J Y, Liu X L, Bo Y X, Shi Z T and Fu Z. 2019. Plastic greenhouse recognition based on GF-2 data and multi-texture features. Transactions of the Chinese Society of Agricultural Engineering, 35(4): 106-113
  27. 27.
    Yang Y J, Tian Q J, Zhan Y L, Tao B and Xu K J. 2018. Effects of spatial resolution and texture features on multi-spectral remote sensing classification. Journal of Geo-Information Science, 20(1): 99-107
  28. 28.
    Yu W W, Xu K J, Zhao P, Shen P J and Zhao Y J. 2021. Influence of red-edge spectrum of sentinel-2 image on identification of dominant tree species in different growing periods. Geography and Geo-Information Science, 37(3): 42-49
  29. 29.
    Zhang D J, Pan Y Z, Zhang J S, Hu T G, Zhao J H, Li N and Chen Q. 2020. A generalized approach based on convolutional neural networks for large area cropland mapping at very high resolution. Remote Sensing of Environment, 247: 111912
  30. 30.
    Zhang J S, Pan Y Z, Han L J, Su W and He Y C. 2007. Land Use/cover Change Detection with Multi-source Data, (04):500-510
  31. 31.
    Zhu S, Zhang J S, Shuai G Y and Yu Q Y. 2014. Winter wheat mapping by soft and hard land use / cover change detection, 18(02):476-496
  32. 32.
    Zhu X F, Li S B and Xiao G F. 2019. Method on extraction of area and distribution of plastic-mulched farmland based on UAV images. Transactions of the Chinese Society of Agricultural Engineering, 35(4): 106-113

Leia o texto completo

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website