A novel hyperspectral image classification approach based on multiresolution segmentation with a few labeled samples
Hyperspectral remote sensing technology becomes more and more popular in recent years which can be applied to satellite, plane, and flying robots. An important application of hyperspectral remote sensing is the classification of ground objects. However, when the number of labeled samples is very sma...
Main Authors: | Binge Cui, Xiudan Ma, Faxi Zhao, Yanan Wu |
---|---|
Format: | Article |
Language: | English |
Published: |
SAGE Publishing
2017-06-01
|
Series: | International Journal of Advanced Robotic Systems |
Online Access: | https://doi.org/10.1177/1729881417710219 |
Similar Items
-
A Sparse Representation-Based Sample Pseudo-Labeling Method for Hyperspectral Image Classification
by: Binge Cui, et al.
Published: (2020-02-01) -
Kernel Supervised Ensemble Classifier for the Classification of Hyperspectral Data Using Few Labeled Samples
by: Jike Chen, et al.
Published: (2016-07-01) -
Boosting Few-Shot Hyperspectral Image Classification Using Pseudo-Label Learning
by: Chen Ding, et al.
Published: (2021-09-01) -
Semi-Supervised Classification of Hyperspectral Images Based on Extended Label Propagation and Rolling Guidance Filtering
by: Binge Cui, et al.
Published: (2018-03-01) -
Intraclass Similarity Structure Representation-Based Hyperspectral Imagery Classification With Few Samples
by: Wei Wei, et al.
Published: (2020-01-01)