Hyperspectral Image Classification Method Based on 2D–3D CNN and Multibranch Feature Fusion
The emergence of a convolutional neural network (CNN) has greatly promoted the development of hyperspectral image (HSI) classification technology. However, the acquisition of HSI is difficult. The lack of training samples is the primary cause of low classification performance. The traditional CNN-ba...
Main Authors: | Zixian Ge, Guo Cao, Xuesong Li, Peng Fu |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9200676/ |
Similar Items
-
Fully Dense Multiscale Fusion Network for Hyperspectral Image Classification
by: Zhe Meng, et al.
Published: (2019-11-01) -
A Simplified 2D-3D CNN Architecture for Hyperspectral Image Classification Based on Spatial–Spectral Fusion
by: Chunyan Yu, et al.
Published: (2020-01-01) -
Comparison of CNN Algorithms on Hyperspectral Image Classification in Agricultural Lands
by: Tien-Heng Hsieh, et al.
Published: (2020-03-01) -
Hyperspectral Image Classification Using a Hybrid 3D-2D Convolutional Neural Networks
by: Saeed Ghaderizadeh, et al.
Published: (2021-01-01) -
Deep Collaborative Attention Network for Hyperspectral Image Classification by Combining 2-D CNN and 3-D CNN
by: Hao Guo, et al.
Published: (2020-01-01)