Fully Convolutional Spectral–Spatial Fusion Network Integrating Supervised Contrastive Learning for Hyperspectral Image Classification
Hyperspectral image classification using deep learning techniques has received great attention in recent years, considering the powerful spatial feature mining ability of deep learning. Fully convolutional network is an effective deep learning architecture that exploits spatial contextual informatio...
Main Authors: | Yifan Shen, Ling Shi, Ji Zhao, Yuting Dong, Lizhe Wang |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2023-01-01
|
Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10265029/ |
Similar Items
-
PolSAR Image Classification Based on Multi-Modal Contrastive Fully Convolutional Network
by: Wenqiang Hua, et al.
Published: (2024-01-01) -
Spectral–Spatial Exploration for Hyperspectral Image Classification via the Fusion of Fully Convolutional Networks
by: Liang Zou, et al.
Published: (2020-01-01) -
Segmentation of Breast Tumors Based on Fully Convolutional Network and Dynamic Contrast Enhanced Magnetic Resonance Image
by: Yue QIU, et al.
Published: (2022-06-01) -
Comparison of Fully Convolutional Networks (FCN) and U-Net for Road Segmentation from High Resolution Imageries
by: Ozan Ozturk, et al.
Published: (2020-12-01) -
Fully Convolutional Networks for Text Understanding in Scene Images
by: Dena Bazazian
Published: (2020-02-01)