A Hybrid 2D/3D Convolutional Neural Network for Hyperspectral Image Classification
- 10.2991/assehr.k.200401.057How to use a DOI?
- hyperspectral image classification, deep learning, 3D convolutional neural network
Hyperspectral image classification is an important and yet challenging task. With the success of deep learning, the 2D or 3D convolutional neural network-based approaches have been proposed to capture either the spectral, or the spatial data embedded in hyperspectral images. However, existing approaches fail to model the spectral-spatial data simultaneously. To cope with this issue, we proposed this novel hybrid Convolutional Neural Network (H-CNN) model which contains a module of 2D/3D CNNs, and a data interaction module to fuse the spectral- spatial data. Rigorous experimental evaluations have been performed on one benchmark dataset. Our experimental results demonstrate that the H-CNN is superior to the state-of-the-art 2D or 3D CNN models in hyperspectral image classification with respect to three widely adopted evaluation criteria, i.e., average accuracy, F1 score and Kappa coefficient.
- © 2020, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Xiaofei Yang AU - Xiaofeng Zhang AU - Shaokai Wang AU - Weihuang Yang PY - 2020 DA - 2020/04/06 TI - A Hybrid 2D/3D Convolutional Neural Network for Hyperspectral Image Classification BT - Proceedings of the International Conference on Education, Economics and Information Management (ICEEIM 2019) PB - Atlantis Press SP - 265 EP - 269 SN - 2352-5398 UR - https://doi.org/10.2991/assehr.k.200401.057 DO - 10.2991/assehr.k.200401.057 ID - Yang2020 ER -