计算机科学
土地覆盖
一致性(知识库)
数据挖掘
领域(数学)
领域(数学分析)
公制(单位)
规范(哲学)
封面(代数)
人工智能
机器学习
土地利用
数学
机械工程
数学分析
运营管理
土木工程
法学
政治学
纯数学
工程类
经济
作者
Ailong Ma,Chenyu Zheng,Junjue Wang,Yanfei Zhong
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: 1-17
被引量:2
标识
DOI:10.1109/tgrs.2023.3265186
摘要
Unsupervised domain adaptive (UDA) land-cover classification has recently gained more and more attention. UDA aimed to learn a model from the annotated source data and the unlabeled target data that can perform well on the target domain. The existing UDA frameworks based on adversarial training and self-training methods have boosted this field a lot. However, these methods almost all originate from the computer vision field, and they ignore the very nature of high-resolution remote sensing (HRS) images. The core insight of this paper is that a good land-cover classification result always has strong local consistency and good global diversity, which makes it possible to construct a metric representing the properties of good land-cover mapping, to improve the existing UDA algorithms. Firstly, based on this finding, we prove that local consistency and global diversity can be measured by the Frobenius norm and nuclear norm, respectively. Secondly, we propose a novel local consistency and global diversity metric (LCGDM), which can be easily integrated into the existing UDA frameworks. Finally, the experiments conducted on the LoveDA data set prove the validity of the proposed metric, which can not only improve the overall land-cover mapping but also the category-wise prediction.
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