空间科学学报2025，Vol.45Issue(2)：397-412,16.DOI:10.11728/cjss2025.02.2024-0146
基于置信学习互导框架的小样本条件下森林扰动类型遥感分类
Forest Disturbance Attribution under Small Sample Conditions Based on Confidence Learning Mutual Guidance Framework
严燕 1吴伶 1李军集 2赵于鑫 1叶昕3
作者信息
- 1. 中国地质大学(北京)信息工程学院 北京 100083
- 2. 中国地质大学(北京)信息工程学院 北京 100083||广西壮族自治区林业科学研究院 南宁 530002
- 3. 中国农业大学信息与电气工程学院 北京 100083
- 折叠
摘要
Abstract
As the core technical means for human beings to carry out scientific research on the Earth system and the application of spatial information in multiple fields,remote sensing technology has com-prehensively improved human beings'understanding of the complex process of changes in the Earth sys-tem,and the use of remote sensing technology with spatial and temporal continuity to carry out distur-bance monitoring and classification research on forest ecosystems,which are important components of the Earth's land surface,can be more accurate and efficient.The use of remote sensing technology with spatial and temporal continuity to monitor and classify disturbances in forest ecosystems,an important component of the Earth's land surface,can be more accurate and efficient.However,Forest disturbance attribution requires a large number of disturbance type samples,and the laborious manual labeling of high-quality samples and the sparseness of the change region itself limit the number of labelable distur-bance samples.In this study,we propose a forest disturbance attribution method under small sample conditions based on the confidence learning mutual guidance framework.In this study,we first used Landsat long time-series remote sensing data to detect forest disturbances between 2000 and 2021 based on the Continuous Change Detection and Classification(CCDC)algorithm to obtain a large amount of unlabeled disturbance data,and then used a small number of manually labeled samples and the mutual guidance framework constructed with Random Forest(RF)and Categorical Boosting(CatBoost)classi-fiers to iteratively filter high-confidence data from the unlabeled disturbance data through confidence learning,expanding the labeled sample size of each other's classifiers,and then guided each other's classi-fications to improve the classification accuracy of forest disturbance attribution.The results show that the overall accuracy of forest disturbance attribution based on the confidence learning mutual guidance framework is 91.4%.Compared with results using only a single classifier,the accuracy is improved by 5%.The method demonstrates excellent performance under small sample conditions and provides an effi-cient and reliable solution for forest disturbance type classification research.关键词
小样本/置信学习/互导框架/森林扰动分类Key words
Small sample/Confidence learning/Mutual guidance framework/Forest disturbance attribution分类
信息技术与安全科学引用本文复制引用
严燕,吴伶,李军集,赵于鑫,叶昕..基于置信学习互导框架的小样本条件下森林扰动类型遥感分类[J].空间科学学报,2025,45(2):397-412,16.
