предыдущий|следующий

Urban classification by multi-feature fusion of hyperspectral image and LiDAR data

  • role： First author第一作者
  • Affiliation：

    The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

    Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China

  • Email：caoqiong@whu.edu.cn
  • Introduction：曹琼，1995年生，女，硕士研究生，研究方向为数据融合。E-mail：caoqiong@whu.edu.cn
CAO Qiong12,  
  • role： Corresponding author通信作者
  • Affiliation：

    The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

    Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China

  • Email：maailong007@whu.edu.cn
  • Introduction：马爱龙，1986年生，男，副研究员，研究方向为智能化遥感影像处理、机器学习。E-mail：maailong007@whu.edu.cn
MA Ailong12*,  
  • Affiliation：

    The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

    Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China

ZHONG Yanfei12,  
  • Affiliation：

    College of Computer Science, China University of Geosciences, Wuhan 430074, China

ZHAO Ji3,  
  • Affiliation：

    Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China

ZHAO Bei4,  
  • Affiliation：

    The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

    Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China

ZHANG Liangpei12

реферат

Land Use/Land Cover (LU/LC) classification of urban areas is of great significance to urban studies and has become a highly important research direction. However, with continuous urbanization and more types of inner cities diversified, single remote sensing image has been unable to meet the requirements of high precision. Therefore, urban LU/LC classification by data fusion has emerged. In this study, hyperspectral images are widely used in urban LU/LC classification because of their abundant spectral information. However, an objective limitation is that similar spectral characters with different elevation cannot be distinguished. LiDAR data can obtain accurate elevation information. Therefore, such data will obtain better classification maps when merged with hyperspectral images. This work proposes an urban LU/LC classification method based on the multi-level fusion of hyperspectral imagery and LiDAR data by using the complementary of their advantages. First, the spectral, spatial, and elevation information extracted from two images are stacked to achieve level fusion. Then, the classification is divided into two frameworks. One framework classifies all pixels of the feature images, while the other uses LiDAR data to extract the building mask and classify the off-building area. Classification maps of this framework are obtained by combining the classification map of the latter framework and the off-building area. The classification results are then obtained by voting the classification results obtained by the two frameworks to complete the decision-level fusion. Finally, the conditional random fields are processed to smoothen the image and remove noise. The data set of 2013 IEEE GRSS data fusion contest was experimented on to verify the effect of the proposed algorithm. The OA was 93.22%, and Kappa was 0.93. The accuracy of the proposed method exceeded 90% in most categories, while the classification accuracy of synthetic grassland, soil, tennis court, and running track was 100%. Experiment results showed that the proposed algorithm greatly improved the classification of buildings, roads, and parking lots. In this study, hyperspectral imagery and LiDAR data are applied to classify LU/LC in urban areas. It also combines feature level and decision level and achieves good results. The following problems will be considered in future works: increasing the accuracy of building extraction to improve the effect of feature-level fusion, considering the increasing intensity of LiDAR point cloud data in feature-level fusion, and increasing the number and diversity of classifiers when using the multiple classifier classification.

ключеви́че слова́

remote sensing;hyperspectral;LiDAR;data fusion;urban LU/LC classification;multi-feature

References

  1. 1.
    Akaike H. 1998. Information theory and an extension of the maximum likelihood principle//Parzen E, Tanabe K and Kitagawa G, eds. Selected Papers of Hirotugu Akaike. New York: Springer: 199-213 [DOI: 10.1007/978-1-4612-1694-0_15]
  2. 2.
    Benediktsson J A, Palmason J A and Sveinsson J R. 2005. Classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Transactions on Geoscience and Remote Sensing, 43(3): 480–491
  3. 3.
    Chen B, Huang B and Xu B. 2017a. Multi-source remotely sensed data fusion for improving land cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 124: 27–39
  4. 4.
    Chen B W, Shi S, Gong W, Zhang Q J, Yang J, Du L, Sun J, Zhang Z B and Song S L. 2017c. Multispectral LiDAR point cloud classification: a two-step approach. Remote Sensing, 9(4): 373
  5. 5.
    Chen Y, Nasrabadi N M and Tran T D. 2011. Hyperspectral image classification using dictionary-based sparse representation. IEEE Transactions on Geoscience and Remote Sensing, 49(10): 3973–3985
  6. 6.
    Chen Y S, Li C Y, Ghamisi P, Jia X P and Gu Y F. 2017b. Deep fusion of remote sensing data for accurate classification. IEEE Geoscience and Remote Sensing Letters, 14(8): 1253–1257
  7. 7.
    Cortes C and Vapnik V. 1995. Support-vector networks. Machine Learning, 20(3): 273–297
  8. 8.
    Fauvel M, Benediktsson J A, Chanussot J and Sveinsson J R. 2008. Spectral and spatial classification of hyperspectral data using svms and morphological profiles. IEEE Transactions on Geoscience and Remote Sensing, 46(11): 3804–3814
  9. 9.
    Galar M, Fernandez A, Barrenechea E, Bustince H and Herrera F. 2012. A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(4): 463–484
  10. 10.
    Ghamisi P, Benediktsson J A and Phinn S. 2014. Fusion of hyperspectral and LiDAR data in classification of urban areas//Proceedings of 2014 IEEE Geoscience and Remote Sensing Symposium. Quebec City, Canada: IEEE: 181-184 [DOI: 10.1109/IGARSS.2014.6946386]
  11. 11.
    Pedram G, Höfle B and Zhu X X. 2017. Hyperspectral and LiDAR data fusion using extinction profiles and deep convolutional neural network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(6): 3011–3024
  12. 12.
    Ghassemian H. 2016. A review of remote sensing image fusion methods. Information Fusion, 32: 75–89
  13. 13.
    Kim H C, Pang S N, Je H M, Kim D and Bang S Y. 2003. Constructing support vector machine ensemble. Pattern Recognition, 36(12): 2757–2767
  14. 14.
    Kittler J, Hatef M, Duin R P W and Matas J. 1998. On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(3): 226–239
  15. 15.
    Lahat D, Adali T and Jutten C. 2015. Multimodal data fusion: an overview of methods, challenges, and prospects. Proceedings of the IEEE, 103(9): 1449–1477
  16. 16.
    Li J, Bioucas-Dias J M and Plaza A. 2011. Hyperspectral image segmentation using a new bayesian approach with active learning. IEEE Transactions on Geoscience and Remote Sensing, 49(10): 3947–3960
  17. 17.
    Liao W Z, Bellens R, Pižurica A, Gautama S and Philips W. 2017. Combining feature fusion and decision fusion for classification of hyperspectral and LiDAR data//Proceedings of 2014 IEEE Geoscience and Remote Sensing Symposium. Quebec City, Canada: IEEE: 1241-1244 [DOI: 10.1109/IGARSS.2014.6946657]
  18. 18.
    Liao W Z, Pižurica A, Bellens R, Gautama S and Philips W. 2015. Generalized graph-based fusion of hyperspectral and LiDAR data using morphological features. IEEE Geoscience and Remote Sensing Letters, 12(3): 552–556
  19. 19.
    Man Q X. 2015. Fusion of Hyperspectral and LiDAR Data for Urban Land Use Classification. Shanghai: East China Normal University.
  20. 20.
    Man Q X, Dong P L and Guo H D. 2015. Pixel- and feature-level fusion of hyperspectral and lidar data for urban land-use classification. International Journal of Remote Sensing, 36(6): 1618–1644
  21. 21.
    Merentitis A, Debes C, Heremans R and Frangiadakis N. 2014. Automatic fusion and classification of hyperspectral and LiDAR data using random forests//Proceedings of 2014 IEEE Geoscience and Remote Sensing Symposium. Quebec City, Canada: IEEE: 1245-1248 [DOI: 10.1109/IGARSS.2014.6946658]
  22. 22.
    Momeni R, Aplin P and Boyd D. 2016. Mapping complex urban land cover from spaceborne imagery: the influence of spatial resolution, spectral band set and classification approach. Remote Sensing, 8(2): 88
  23. 23.
    Rasti B, Ghamisi P and Gloaguen R. 2017a. Hyperspectral and LiDAR fusion using extinction profiles and total variation component analysis. IEEE Transactions on Geoscience and Remote Sensing, 55(7): 3997–4007
  24. 24.
    Rasti B, Ghamisi P, Plaza J and Plaza A. 2017b. Fusion of hyperspectral and LiDAR data using sparse and low-rank component analysis. IEEE Transactions on Geoscience and Remote Sensing, 55(11): 6354–6365
  25. 25.
    Tong Q X, Zhang B and Zheng L F. 2006. Hyperspectral Remote Sensing. Beijing: Higher Education Press
  26. 26.
    Wu H and Prasad S. 2013. Infinite Gaussian mixture models for robust decision fusion of hyperspectral imagery and full waveform LiDAR data//Proceedings of 2013 IEEE Global Conference on Signal and Information Processing. Austin, USA: IEEE: 1025-1028 [DOI: 10.1109/GlobalSIP.2013.6737068]
  27. 27.
    Xu X D, Li W, Ran Q, Du Q, Gao L R and Zhang B. 2018. Multisource remote sensing data classification based on convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, 56(2): 937–949
  28. 28.
    Zhang J X, Lin X G and Ning X G. 2013. SVM-based classification of segmented airborne LiDAR point clouds in urban areas. Remote Sensing, 5(8): 3749–3775
  29. 29.
    Zhang L P and Shen H F. 2016. Progress and future of remote sensing data fusion. Journal of Remote Sensing, 20(5): 1050–1061
  30. 30.
    Zhao J. 2017. Conditional Random Fields for High Resolution Remote Sensing Image Classification. Wuhan: Wuhan University
  31. 31.
    Zhao J, Zhong Y F, Shu H and Zhang L P. 2016. High-resolution image classification integrating spectral-spatial-location cues by conditional random fields. IEEE Transactions on Image Processing, 25(9): 4033–4045
  32. 32.
    Zhao J, Zhong Y F and Zhang L P. 2015. Detail-preserving smoothing classifier based on conditional random fields for high spatial resolution remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 53(5): 2440–2452
  33. 33.
    Zhong Y F, Cao Q, Zhao J, Ma A L, Zhao B and Zhang L P. 2017a. Optimal decision fusion for urban land-use/land-cover classification based on adaptive differential evolution using hyperspectral and LiDAR data. Remote Sensing, 9(8): 868
  34. 34.
    Zhong Y F, Jia T Y, Zhao J, Wang X Y and Jin S Y. 2017b. Spatial-spectral-emissivity land-cover classification fusing visible and thermal infrared hyperspectral imagery. Remote Sensing, 9(9): 910
  35. 35.
    Zhong Y F, Ma A L, Ong Y S, Zhu Z X and Zhang L P. 2018. Computational intelligence in optical remote sensing image processing. Applied Soft Computing, 64: 75–93

Читать полностью

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