计算机科学
人工智能
编码(集合论)
特征提取
变更检测
监督学习
模式识别(心理学)
标记数据
半监督学习
深度学习
图像(数学)
机器学习
共同训练
人工神经网络
集合(抽象数据类型)
程序设计语言
作者
Jia-Xin Wang,Teng Li,Si-Bao Chen,Jin Tang,Bin Luo,Richard C. Wilson
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:60: 1-13
被引量:17
标识
DOI:10.1109/tgrs.2022.3228016
摘要
With the development of deep learning in remote sensing (RS) image change detection (CD), the dependence of CD models on labeled data has become an important problem. To make better use of the comparatively resource-saving unlabeled data, the CD method based on semi-supervised learning (SSL) is worth further study. This article proposes a reliable contrastive learning (RCL) method for semi-supervised RS image CD. First, according to the task characteristics of CD, we design the contrastive loss based on the changed areas to enhance the model’s feature extraction ability for changed objects. Then, to improve the quality of pseudo labels in SSL, we use the uncertainty of unlabeled data to select reliable pseudo labels for model training. Combining these methods, semi-supervised CD models can make full use of unlabeled data. Extensive experiments on three widely used CD datasets demonstrate the effectiveness of the proposed method. The results show that our semi-supervised approach has a better performance than related methods. The code is available at https://github.com/VCISwang/RC-Change-Detection .
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