TY - GEN
T1 - Fusion of Hyperspectral and Lidar Data Based on Dual-Branch Convolutional Neural Network
AU - Wang, Jinzhe
AU - Zhang, Junping
AU - Guo, Qingle
AU - Li, Tong
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - With to the development of sensors, the fusion of features from multisource data becomes an interesting but challenging problem. In this paper, the fusion of hyperspectral imagery (HSI) and light detection and ranging (LiDAR) data is investigated with a novel and simplified deep learning architecture, named the dual-branch convolutional neural network (DB-CNN). More specifically, a 3D CNN framework as one of the two branches is used to extract spectral-spatial features simultaneously from HSI, which can keep three-dimensional structural characteristics of HSI. Another one is 2D CNN with cascade blocks, which is developed to extract elevation feature from LiDAR data, and it can exploit the multiscale features. Finally, the features of two branches will be flattened and stacked, and then sent to the fully connected layers. The experiments show that the proposed DB-CNN method can effectively fuse the HSI and LiDAR data, and yield higher classification performance than some existing methods.
AB - With to the development of sensors, the fusion of features from multisource data becomes an interesting but challenging problem. In this paper, the fusion of hyperspectral imagery (HSI) and light detection and ranging (LiDAR) data is investigated with a novel and simplified deep learning architecture, named the dual-branch convolutional neural network (DB-CNN). More specifically, a 3D CNN framework as one of the two branches is used to extract spectral-spatial features simultaneously from HSI, which can keep three-dimensional structural characteristics of HSI. Another one is 2D CNN with cascade blocks, which is developed to extract elevation feature from LiDAR data, and it can exploit the multiscale features. Finally, the features of two branches will be flattened and stacked, and then sent to the fully connected layers. The experiments show that the proposed DB-CNN method can effectively fuse the HSI and LiDAR data, and yield higher classification performance than some existing methods.
KW - data fusion
KW - deep learning
KW - dual-branch CNN
KW - feature extraction
UR - https://www.scopus.com/pages/publications/85077681905
U2 - 10.1109/IGARSS.2019.8899332
DO - 10.1109/IGARSS.2019.8899332
M3 - 会议稿件
AN - SCOPUS:85077681905
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 3388
EP - 3391
BT - 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Y2 - 28 July 2019 through 2 August 2019
ER -