@inproceedings{c4aa61a8436b42e6b279bcf954d18af8,
title = "Collaborative Classification of Hyperspectral and Lidar Data with Information Fusion and Deep Nets",
abstract = "Convolutional neural network (CNN) receives extensive attention in hyperspectral image classification. While hyper-spectral images contain abundant spectral information but lack spatial information, which usually contributes to poor classification results. In this paper, a novel classification framework called information fusion based CNN (IF-CNN) is proposed to compensate for the shortcomings of hyper-spectral images. The proposed method merges hyperspectral images with abundant spectral information and LiDAR images with rich spatial information as the input of classification framework. Furthermore, the framework consists of two convolutional neural networks: one-dimensional CNN for extracting spectral features, and two-dimensional CNN for extracting spatial correlation features. Experimental results demonstrate that the proposed method achieves excellent performance compared with some existing methods.",
keywords = "Convolutional Neural Network, Deep Learning, Hyperspectral Image, Information Fusion, Pattern Recognition",
author = "Chen Chen and Xudong Zhao and Wei Li and Ran Tao and Qian Du",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 ; Conference date: 28-07-2019 Through 02-08-2019",
year = "2019",
month = jul,
doi = "10.1109/IGARSS.2019.8898443",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2475--2478",
booktitle = "2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings",
address = "United States",
}