Semi-Supervised PolSAR Image Classification Based on Self-Training and Superpixels
Polarimetric synthetic aperture radar (PolSAR) image classification is a recent technology with great practical value in the field of remote sensing. However, due to the time-consuming and labor-intensive data collection, there are few labeled datasets available. Furthermore, most available state-of...
Main Authors: | Yangyang Li, Ruoting Xing, Licheng Jiao, Yanqiao Chen, Yingte Chai, Naresh Marturi, Ronghua Shang |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-08-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/11/16/1933 |
Similar Items
-
Superpixel-Based Hybrid Discriminative Random Field for Fast PolSAR Image Classification
by: Wanying Song, et al.
Published: (2019-01-01) -
PolSAR Image Classification via Learned Superpixels and QCNN Integrating Color Features
by: Xinzheng Zhang, et al.
Published: (2019-08-01) -
Polarimetric Contextual Classification of PolSAR Images Using Sparse Representation and Superpixels
by: Jilan Feng, et al.
Published: (2014-07-01) -
PolSAR Image Classification Using a Superpixel-Based Composite Kernel and Elastic Net
by: Yice Cao, et al.
Published: (2021-01-01) -
Fuzzy Superpixels based Semi-supervised Similarity-constrained CNN for PolSAR Image Classification
by: Yuwei Guo, et al.
Published: (2020-05-01)