计算机科学
判别式
领域(数学分析)
人工智能
代表(政治)
模式识别(心理学)
分割
编码器
上下文图像分类
比例(比率)
边距(机器学习)
图像(数学)
域适应
数据挖掘
机器学习
数学
数学分析
物理
量子力学
政治
政治学
分类器(UML)
法学
操作系统
作者
Maoguo Gong,Wenyuan Qiao,Hao Li,A. K. Qin,Tianqi Gao,Tianshi Luo,Lining Xing
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:61: 1-16
标识
DOI:10.1109/tgrs.2023.3302430
摘要
Classification of remote sensing image series which differ in quality and details, has impportant implications for the analysis of land cover, whereas it is expensive and time-consuming as a result of manual annotations. Fortunately, domain adaptation (DA) provides an outstanding solution to the problem. However, information loss while aligning two distributions often exists in traditional DA methods, which impacts the effect of classification with DA. To alleviate this issue, an inter-domain representation alignment and fine-tuning based network (RAFNet) is proposed for image series classification. Inter-domain representation alignment, which is fulfilled by a variational auto-encoder (VAE) trained by both source and target data, encourages reducing the discrepancy between the two marginal distributions of different domains and simultaneously preserving more data properties. As a result, RAFNet, which fuses the multi-scale aligned representations, performs classification task in the target domain after well trained with supervised learning in the source domain. Specifically, the multi-scale aligned representations of RAFNet is acquired by duplicating the frozen encoder of VAE. Then, an information based loss function is designed to fine-tune RAFNet, in which both the unchanged and changed information implied in change maps is completely used to learn the discriminative features better and make the model more generalized for the target domain. Finally, experiment studies on three datasets validate the effectiveness of RAFNet with considerable segmentation accuracy even the target data has no access to any annotated information.
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