期刊
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
卷 10, 期 6, 页码 3011-3024出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2016.2634863
关键词
Convolutional neural network (CNN); deep learning; extinction profile (EP); graph-based feature fusion (GBFF); hyperspectral; light detection and ranging (LiDAR); random forest (RF); support vector machines (SVMs)
类别
资金
- Alexander von Humboldt Fellowship
- Helmholtz Young Investigators Group [VH-NG-1018]
- Division Of Earth Sciences
- Directorate For Geosciences [1339015] Funding Source: National Science Foundation
This paper proposes a novel framework for the fusion of hyperspectral and light detection and ranging-derived rasterized data using extinction profiles (EPs) and deep learning. In order to extract spatial and elevation information from both the sources, EPs that include different attributes (e.g., height, area, volume, diagonal of the bounding box, and standard deviation) are taken into account. Then, the derived features are fused via either feature stacking or graph-based feature fusion. Finally, the fused features are fed to a deep learning-based classifier (convolutional neural network with logistic regression) to ultimately produce the classification map. The proposed approach is applied to two datasets acquired in Houston, TX, USA, and Trento, Italy. Results indicate that the proposed approach can achieve accurate classification results compared to other approaches. It should be noted that, in this paper, the concept of deep learning has been used for the first time to fuse LiDAR and hyperspectral features, which provides new opportunities for further research.
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