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

Liao, Wenzhi and Vancoillie, Frieke and Gao, Lianru and Li, Liwei and Zhang, Bing and Chanussot, Jocelyn (2018) Deep learning for fusion of APEX hyperspectral and full-waveform LiDAR remote sensing data for tree species mapping. IEEE Access, 6. pp. 68716-68729. ISSN 2169-3536 (https://doi.org/10.1109/ACCESS.2018.2880083)

[thumbnail of Liao-etal-IA2018-Deep-learning-fusion-APEX-hyperspectral-full-waveform-LiDAR-remote-sensing-data-tree-species-mapping]
Preview
 Text. Filename: Liao_etal_IA2018_Deep_learning_fusion_APEX_hyperspectral_full_waveform_LiDAR_remote_sensing_data_tree_species_mapping.pdf
Final Published Version
Download (3MB)| Preview

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.

CORE (COnnecting REpositories)