.png){kind=icon}
返回
Consistency Regularization Semisupervised Learning for PolSAR Image Classification
.png){kind=icon}
OA
AI
DOI：10.1155/int/7261699.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Polarimetric Synthetic Aperture Radar (PolSAR) images have emerged as an important data source for land cover classification research due to their all-weather, all-day monitoring capabilities. Deep learning-based classification methods have recently gained significant attention in PolSAR image classification since they have demonstrated excellent performance in the computer vision field. However, the main issue with deep learning-based methods is that they require large amounts of training data. Additionally, the scarcity of labeled data is a significant challenge in the PolSAR image field. Therefore, in this article, we proposed an advanced semisupervised deep self-training algorithm for PolSAR image classification, which utilized both labeled and unlabeled data in a semisupervised way. Then, a training optimization method and a high-confidence sample selection strategy are proposed by integrating consistency regularization. In addition, to achieve stronger feature extraction capabilities, we designed a deep learning-based classifier that combines residual blocks with an efficient multiscale attention module. We have conducted experiments on three popular real PolSAR datasets: 1989 Flevoland, 1991 Flevoland, and Oberpfaffenhofen. The classification results on these datasets demonstrated that the proposed method outperforms several other comparison algorithms, with overall accuracy up to 99.3%, 99.15%, and 94.12%, respectively. These results demonstrated the effectiveness of the proposed method for PolSAR image classification.
Keyword:
consistency regularization
image classification
pseudo-label selection
self-training
semisupervised
期刊
IF:
3.7
论文数:
3.1K
被引数:
8.1K
机构
引用论文
.png){kind=icon}
A Graph-Based Semisupervised Deep Learning Model for PolSAR Image Classification基于图的半监督深度学习PolSAR图像分类模型
PREAI
分享收藏Arsenic-HypertolerantPseudomonadsIsolated from Ancient Gold and Copper-Bearing Black Shale Deposits
PREAI
分享收藏Complex-Valued Convolutional Neural Network and Its Application in Polarimetric SAR Image Classification复值卷积神经网络及其在极化SAR图像分类中的应用
PREAI
分享收藏
查看更多内容