Semi-supervised rotation forest based on ensemble margin theory for the classification of hyperspectral image with limited training data-Reference-Cited by-同舟云学术

Semi-supervised rotation forest based on ensemble margin theory for the classification of hyperspectral image with limited training data

Published:2021-10 Issue: Volume:575 Page:611-638
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Feng Wei,Quan Yinghui,Dauphin Gabriel,Li Qiang,Gao Lianru,Huang Wenjiang,Xia Junshi,Zhu Wentao,Xing Mengdao

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference46 articles.

1. Fast spectral clustering with anchor graph for large hyperspectral images;Wang;IEEE Geosci. Remote Sens. Lett.,2017

2. Classification Methods for Remotely Sensed Data,2016

3. Weight-based rotation forest for hyperspectral image classification;Feng;IEEE Geosci. Remote Sens. Lett.,2017

4. Sparse graph regularization for hyperspectral remote sensing image classification;Xue;IEEE Trans. Geosci. Remote Sens.,2017

5. Class imbalance ensemble learning based on the margin theory;Feng;Appl. Sci.,2018

Cited by 31 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Lightweight omni-dimensional dynamic convolution with spatial-spectral self-attention network for hyperspectral image classification;International Journal of Remote Sensing;2024-08-30

2. Multiobjective band selection approach via an adaptive particle swarm optimizer for remote sensing hyperspectral images;Swarm and Evolutionary Computation;2024-08

3. A Stratified Mislabeled Instances Removal Method Based on Density Spatial Clustering for Hyperspectral Image Classification;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

4. Hyperspectral band selection via region-wise latent feature fusion and graph filter embedded subspace clustering;Engineering Applications of Artificial Intelligence;2024-06

5. Ensemble synthetic oversampling with pixel pair for class-imbalanced and small-sized hyperspectral data classification;International Journal of Applied Earth Observation and Geoinformation;2024-04