Hyperspectral and LiDAR Data Fusion Using Extinction Profiles and Deep Convolutional Neural Network

Pedram Ghamisi, Bernhard Hofle, Xiao Xiang Zhu
2017 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
This paper proposes a novel framework for the fusion of hyperspectral and LiDAR-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
more » ... 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 data sets acquired in Houston, USA and Trento, Italy. Results indicate that the proposed approach can achieve accurate classification results compared to other approaches. Index Terms-Convolutional neural network, deep learning, extinction profile, graph-based feature fusion, hyperspectral, LiDAR, random forest, support vector machines.
doi:10.1109/jstars.2016.2634863 fatcat:ft2d3fc6tnfcvkwvfcctthjl5u