宁夏大学学报（自然科学版）2024，Vol.45Issue(3)：325-332,8.
基于CNN-Transformer半监督交叉学习的遥感图像场景分类方法
A Remote Sensing Image Scene Classification Based on CNN-Transformer Semi-Supervised Cross Learning
摘要
Abstract
With the development of deep learning technology,deep learning methods based on Convolutional Neural Networks(CNN)and Transformers have received extensive attention and research in fully supervised remote sensing image scene classification tasks.However,achieving good classification performance with lim-ited labeled samples remains challenging.Considering the differences in deep feature extraction methods between CNN and Transformers,a semi-supervised cross-learning method for remote sensing image scene clas-sification(SCL-CTNet)was proposed.By constructing consistency constraints on the outputs of CNN and Transformers,information from unlabeled data to guide model training would be better extracted.The semi-supervised cross learning method utilizes the output of weakly augmented images in one network as pseudo-labels to supervise the predictions of strongly augmented images in another network,fully leveraging the local-global information of unlabeled samples,encouraging consistency in predictions for the same input image between the two networks,and enhancing model generalization.Adaptive thresholding is used to filter pseudo-labels,improving their reliability.Experimental results on the AID and NWPU-RESISC45 datasets demon-strate the effectiveness of the proposed method.关键词
高分辨率遥感图像/场景分类/卷积神经网络/Transformer/半监督学习Key words
high resolution remote sensing images/scene classification/convolutional neural networks/trans-former/semi-supervised learning分类
信息技术与安全科学引用本文复制引用
单飞龙,吕鹏远,李梦晨..基于CNN-Transformer半监督交叉学习的遥感图像场景分类方法[J].宁夏大学学报（自然科学版）,2024,45(3):325-332,8.基金项目
国家自然科学基金资助项目(42001307) （42001307）
