Improved Active Deep Learning for Semi-Supervised Classification of Hyperspectral Image
Hyperspectral image (HSI) data classification often faces the problem of the scarcity of labeled samples, which is considered to be one of the major challenges in the field of remote sensing. Although active deep networks have been successfully applied in semi-supervised classification tasks to addr...
Main Authors: | Qingyan Wang, Meng Chen, Junping Zhang, Shouqiang Kang, Yujing Wang |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-12-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/14/1/171 |
Similar Items
-
Graph-Based Semisupervised Learning With Weighted Features for Hyperspectral Remote Sensing Image Classification
by: Qingyan Wang, et al.
Published: (2022-01-01) -
Double-Branch Multilevel Skip Sparse Graph Attention Network for Hyperspectral Image Classification
by: Qingyan Wang, et al.
Published: (2024-01-01) -
Graph-Based Semi-Supervised Learning With Tensor Embeddings for Hyperspectral Data Classification
by: Ioannis Georgoulas, et al.
Published: (2023-01-01) -
ES<sup>2</sup>FL: Ensemble Self-Supervised Feature Learning for Small Sample Classification of Hyperspectral Images
by: Bing Liu, et al.
Published: (2022-08-01) -
A review on graph-based semi-supervised learning methods for hyperspectral image classification
by: Shrutika S. Sawant, et al.
Published: (2020-08-01)