@inproceedings{9d924d749f754c9a924f640152c2458f,
title = "Deep fusion of hyperspectral and LiDAR data for thematic classification",
abstract = "Recently, the fusion of hyperspectral and light detection and ranging (LiDAR) data has obtained a great attention in the remote sensing community. In this paper, we propose a new feature fusion framework using deep neural network (DNN). The proposed framework employs a novel 3D convolutional neural network (CNN) to extract the spectral-spatial features of hyperspectral data, a deep 2D CNN to extract the elevation features of LiDAR data, and then a fully connected deep neural network to fuse the extracted features in the previous CNNs. Through the aforementioned three deep networks, one can extract the discriminant and invariant features of hyperspectral and LiDAR data. At last, logistic regression is used to produce the final classification results. The experimental results reveal that the proposed deep fusion model provides competitive results. Furthermore, the proposed deep fusion idea opens a new window for future research.",
keywords = "Convolutional neural network, LiDAR, data fusion, deep neural network, feature extraction, hyperspectral",
author = "Yushi Chen and Chunyang Li and Pedram Ghamisi and Chunyu Shi and Yanfeng Gu",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 ; Conference date: 10-07-2016 Through 15-07-2016",
year = "2016",
month = nov,
day = "1",
doi = "10.1109/IGARSS.2016.7729930",
language = "英语",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3591--3594",
booktitle = "2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings",
address = "美国",
}