Hyperspectral Image Classification With Spectral and Spatial Graph Using Inductive Representation Learning Network
Convolutional neural networks (CNN) have achieved excellent performance for the hyperspectral image (HSI) classification problem due to better extracting spectral and spatial information. However, CNN can only perform convolution calculations on Euclidean datasets. To solve this problem, recently, t...
Main Authors: | Pan Yang, Lei Tong, Bin Qian, Zheng Gao, Jing Yu, Chuangbai Xiao |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
|
Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9286488/ |
Similar Items
-
Incremental Graph Embedding Based on Spatial-Spectral Neighbors for Hyperspectral Image Classification
by: Dongqing Li, et al.
Published: (2018-01-01) -
Graph inductive learning method for small sample classification of hyperspectral remote sensing images
by: Xibing Zuo, et al.
Published: (2020-01-01) -
Spectral-Spatial Classification of Hyperspectral Images Using Joint Bilateral Filter and Graph Cut Based Model
by: Yi Wang, et al.
Published: (2016-09-01) -
Dimensionality Reduction of Hyperspectral Image with Graph-Based Discriminant Analysis Considering Spectral Similarity
by: Fubiao Feng, et al.
Published: (2017-03-01) -
Dual Graph U-Nets for Hyperspectral Image Classification
by: Fangming Guo, et al.
Published: (2021-01-01)