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Deep Learning Network Integrated Multi-spectral Data and Interferometric Imaging Radar Altimeter Data of Tiangong-2 for Land Use Classification

  • Kang Liu
  • Zhuang Zhou
  • Xue Wan
  • Baoqin Hei
  • Shaopan Xiong
  • Chunying Wu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 541)

Abstract

Multispectral data and radar data contain abundant spectral and texture features, which are widely used in land use classification. High precision registration and information mining are the two major difficulties faced by the classification of these two data. In this paper, visible and near infrared spectrum range of Wide-band Imaging Spectrometer (VNI) and Interferometric Imaging Radar Altimeterdata (InIRA) are used as raw data, and data registration and combination is carried out (the combination data is called for VNI_InIRA). Then the information of each pixel before and after combination is extracted and transformed into gray-scale image which is easy to be input by convolution neural network (CNN). Finally, the classification of land use is carried out by trained CNN models, and the accuracy is verified. The results show that the classification results of VNI_InIRA data are better than those of VNI data. The overall classification accuracy is 94.29% and 91.83% respectively. Therefore, the CNN which synthesizes the spectral characteristics of VNI data and the texture topographic features of InIRA data is a feasible method to obtain accurate land use classification information, and to provide a reference for the study of land use classification extraction in coastal areas.

Keywords

Deep learning Convolutional Neural Network Tiangong-2 Multi-spectral data Interferometric Imaging Radar Altimeter data Land use classification 

Notes

Acknowledgement

Thanks to China Manned Space Engineering for providing space science and application data products of Tiangong-2. Thanks to the National R&D Infrastructure and Facility Development Program of China, “Fundamental Science Data Sharing Platform” (DKA2018-12-02-23) who funded the project.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Kang Liu
    • 1
  • Zhuang Zhou
    • 1
  • Xue Wan
    • 1
  • Baoqin Hei
    • 1
  • Shaopan Xiong
    • 1
    • 2
  • Chunying Wu
    • 1
  1. 1.Key Laboratory of Space UtilizationTechnology and Engineering Center for Space Utilization, Chinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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