Comparison of Land Cover Types Classification Methods Using Tiangong-2 Multispectral Image

  • Lei Yu
  • Jinhui LanEmail author
  • Yiliang Zeng
  • Jinlin Zou
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 541)


In this paper, Qinghai Lake and Taihu Lake are used as experimental areas, and the visible and near infrared spectrum range of Tiangong-2 Wide-band Imaging Spectrometer are selected for classification research. On the basis of preprocessing, the images are classified by several common classification methods such as Minimum Distance Classification (MDC), Maximum Likelihood Classification (MLC), Spectral Angle Mapping (SAM) and Support Vector Machine (SVM). The classification results are verified using confusion matrices. In the land cover types classification of Qinghai Lake area, the overall classification accuracy of SVM is the highest, which is 99.04%, followed by SAM of 98.78%, MDC of 97.84%, and MLC of 86.89%. In the land cover types classification of Taihu Lake area, the overall classification accuracy of SVM is the highest, which is 92.44%, followed by MDC of 88.90%, SAM of 84.01%, and MLC of 71.01%. After comparative analysis, the practicality and superiority of the SVM method in the image classification of visible and near infrared spectrum range of Wide-band Imaging Spectrometer are proved, which provides a technical reference and theoretical basis for the classification research of Tiangong-2 data.


Tiangong-2 Multispectral Classification SVM 



The work is supported by the Major Special Project of the China High-Resolution Earth Observation System. Thanks to China Manned Space Engineering for providing Wide-band Imaging Spectrometer data products of Tiangong-2. We would like to thank the editor and reviewers for their reviews that improved the content of this paper.


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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Automation and Electrical EngineeringUniversity of Science and TechnologyBeijingChina

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