Deep learning for fusion of APEX hyperspectral and full-waveform LiDAR remote sensing data for tree species mapping

Wenzhi Liao, Frieke Vancoillie, Lianru Gao, Liwei Li, Bing Zhang, Jocelyn Chanussot, Michael Pecht (Editor)

Research output: Contribution to journalArticle

6 Citations (Scopus)
28 Downloads (Pure)

Abstract

Deep learning has been widely used to fuse multi-sensor data for classification. However, current deep learning architecture for multi-sensor data fusion might not always perform better than single data source, especially for the fusion of hyperspectral and light detection and ranging (LiDAR) remote sensing data for tree species mapping in complex, closed forest canopies. In this paper, we propose a new deep fusion framework to integrate the complementary information from hyperspectral and LiDAR data for tree species mapping. We also investigate the fusion of either “single-band” or multi-band (i.e., full-waveform) LiDAR with hyperspectral data for tree species mapping. Additionally, we provide a solution to estimate the crown size of tree species by the fusion of multi-sensor data. Experimental results on fusing real APEX hyperspectral and LiDAR data demonstrate the effectiveness of the proposed deep fusion framework. Compared to using only single data source or current deep fusion architecture, our proposed method yields improvements in overall and average classification accuracies ranging from 82.21% to 87.10% and 76.71% to 83.45%, respectively.
Original languageEnglish
Pages (from-to)68716-68729
Number of pages14
JournalIEEE Access
Volume6
DOIs
Publication statusPublished - 9 Nov 2018

Keywords

  • LiDAR
  • multi-sensor data
  • remote sensing
  • tree species mapping
  • deep learning
  • data fusion
  • hyperspectral
  • image classification
  • forestry
  • vegetation

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    Liao, W., Vancoillie, F., Gao, L., Li, L., Zhang, B., Chanussot, J., & Pecht, M. (Ed.) (2018). Deep learning for fusion of APEX hyperspectral and full-waveform LiDAR remote sensing data for tree species mapping. IEEE Access, 6, 68716-68729. https://doi.org/10.1109/ACCESS.2018.2880083